---------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Dropbox\bavarian affair\merged data\replication\tables\replication_OLPR
> .log
  log type:  text
 opened on:  20 Nov 2015, 19:24:50

. 
. *********************************************
. * Table 3: Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Reg
> ression Solution
. *********************************************
. 
. 
. use tables\candidates_csu_replication.dta, clear

. keep if year==2013
(73 observations deleted)

. 
. global listdum = "list_f list_s list_t" 

. global list = "list_pre listN_cand"

. global role = "gov2 frontrunner bezirksvorsitz01 parteiamt local_committee"

. global demo = "title cand_age gender" 

. global comp = "i.leg incumb_2000 nachruecker incumbency_stk  district_candidate" 

. 
. eststo clear

. 
. quietly eststo :  reg sv_cand affair $listdum , r

. quietly eststo :  reg sv_cand affair $listdum $list $role $demo $comp i.rbez, r

. quietly eststo :  reg sv_cand affair_cont $listdum , r

. quietly eststo :  reg sv_cand affair_cont $listdum $list $role $demo $comp i.rbez, r

. 
. esttab, compress b(2) se(2) star(* 0.05) label order(*affair*)  /// 
> rename(affair_cont affair) /// 
> indicate("Controls for list and candidate quality and OLPR district = $list $role tit
> le gender cand_age *leg* incumb_2000  nachruecker incumbency_stk  district_candidate 
> *rbez*") ///
> title({\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party List
> s - Regression Solution}) ///
> note("Note: Regression with robust standard errors on 2013 second vote shares of CSU 
> candidates within their respective party list. Treatment indicator with binary (Model
> s 1-3) or continuous specification (Models 4-6). Controls (used where indicated) incl
> ude dummies for first, second and third list position, absolute list position, length
>  of list, dummies for candidates being member of local interest committee, cabinet me
> mber, regional party leader, leading party functionary, CSU frontrunner, district inc
> umbent, being district candidate, having academic titles, being female, being incumbe
> nt since 2000, the number of legislative periods, age in years as well as dummies for
>  the seven OLPR ballots (electoral districts) of Bavaria.") ///
> mtitles("binary treat." "binary treat." "cont. treat." "cont. treat.") ///
> collabels("Cand. second vote share" ,lhs(Dep. var.: Within CSU list vote))

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Regr
> ession Solution}
------------------------------------------------------------
                       (1)        (2)        (3)        (4) 
                      bi..       bi..  co.. tr~.  co.. tr~. 
Dep. var.: Wit~e Ca.. se~e  Ca.. se~e  Ca.. se~e  Ca.. se~e 
------------------------------------------------------------
CSU affair ~2013     -3.61*     -4.69*     -4.13*     -5.47*
                    (1.77)     (2.13)     (1.94)     (2.61) 

First bal..          42.43*     31.76*     43.02*     31.32*
                    (5.65)     (4.56)     (5.61)     (4.58) 

Second ba..          12.64*      8.23*     13.28*      8.42*
                    (4.27)     (3.19)     (4.29)     (3.05) 

Third bal..           2.12*      0.15       2.79*      0.63 
                    (0.61)     (1.17)     (0.96)     (1.16) 

Constant              2.23*      6.00       2.13*      5.72 
                    (0.21)     (5.01)     (0.20)     (4.82) 

Controls for l~         No        Yes         No        Yes 
------------------------------------------------------------
Observations           164        164        164        164 
------------------------------------------------------------
Note: Regression with robust standard errors on 2013 second vote shares of CSU candidat
> es within their respective party list. Treatment indicator with binary (Models 1-3) o
> r continuous specification (Models 4-6). Controls (used where indicated) include dumm
> ies for first, second and third list position, absolute list position, length of list
> , dummies for candidates being member of local interest committee, cabinet member, re
> gional party leader, leading party functionary, CSU frontrunner, district incumbent, 
> being district candidate, having academic titles, being female, being incumbent since
>  2000, the number of legislative periods, age in years as well as dummies for the sev
> en OLPR ballots (electoral districts) of Bavaria.
* p<0.05

. 
. 
. 
. *********************************************
. *Table 4: Impact of Affair on Vote Shares of Candidates within CSU Party Lists - CEM 
> Matching Solution 
. *********************************************
. 
. use tables\candidates_csu_replication.dta, clear

. keep if year==2013
(73 observations deleted)

. 
. eststo clear

. 
. **
. *Model 1: matching on controls as specified below, comp. to regression esp. not OLPR 
> list length, local interest comitee, incumbent since 2000 
. 
. cem list_pre(1.5 2.5 3.5 6.5 10.5) gov2 frontrunner bezirksvorsitz01 parteiamt title 
> cand_age(20 30 45 60 80) gender district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rb
> ez_d5 rbez_d6 rbez_d7 , treatment(affair)

Matching Summary:
-----------------
Number of strata: 114
Number of matched strata: 4

             0    1
      All  150   14
  Matched   12    4
Unmatched  138   10


Multivariate L1 distance: .17857143

Univariate imbalance:

                         L1     mean      min      25%      50%      75%      max
          list_pre   .14286   4.8036        1       -2        2        3       -2
              gov2        0        0        0        0        0        0        0
       frontrunner        0        0        0        0        0        0        0
  bezirksvorsitz01        0        0        0        0        0        0        0
         parteiamt        0        0        0        0        0        0        0
             title        0        0        0        0        0        0        0
          cand_age   .03571   1.7143        7        6        1       -1       -9
            gender        0        0        0        0        0        0        0
district_candidate        0        0        0        0        0        0        0
           rbez_d1        0  5.6e-17        0        0        0        0        0
           rbez_d2        0        0        0        0        0        0        0
           rbez_d3        0  8.3e-17        0        0        0       -1        0
           rbez_d4        0        0        0        0        0        0        0
           rbez_d5        0        0        0        0        0        0        0
           rbez_d6        0        0        0        0        0        0        0
           rbez_d7        0  2.8e-17        0        0        0        0        0

. eststo c: reg sv_cand affair list_pre gov2 frontrunner bezirksvorsitz01 parteiamt tit
> le cand_age gender district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6
>  rbez_d7 [iweight=cem_weights]
note: gov2 omitted because of collinearity
note: frontrunner omitted because of collinearity
note: bezirksvorsitz01 omitted because of collinearity
note: parteiamt omitted because of collinearity
note: title omitted because of collinearity
note: gender omitted because of collinearity
note: district_candidate omitted because of collinearity
note: rbez_d2 omitted because of collinearity
note: rbez_d4 omitted because of collinearity
note: rbez_d5 omitted because of collinearity
note: rbez_d6 omitted because of collinearity
note: rbez_d7 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =        16
-------------+----------------------------------   F(5, 10)        =      7.74
       Model |  16.6889418         5  3.33778836   Prob > F        =    0.0032
    Residual |  4.31498775        10  .431498775   R-squared       =    0.7946
-------------+----------------------------------   Adj R-squared   =    0.6918
       Total |  21.0039295        15  1.40026197   Root MSE        =    .65689

------------------------------------------------------------------------------------
           sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
            affair |  -1.288774    .407553    -3.16   0.010    -2.196858    -.380689
          list_pre |   .0142104    .031486     0.45   0.661    -.0559448    .0843656
              gov2 |          0  (omitted)
       frontrunner |          0  (omitted)
  bezirksvorsitz01 |          0  (omitted)
         parteiamt |          0  (omitted)
             title |          0  (omitted)
          cand_age |  -.0338098    .026818    -1.26   0.236    -.0935641    .0259445
            gender |          0  (omitted)
district_candidate |          0  (omitted)
           rbez_d1 |   1.294229   .4175636     3.10   0.011     .3638394    2.224619
           rbez_d2 |          0  (omitted)
           rbez_d3 |  -.9787026   .6746656    -1.45   0.178    -2.481951     .524546
           rbez_d4 |          0  (omitted)
           rbez_d5 |          0  (omitted)
           rbez_d6 |          0  (omitted)
           rbez_d7 |          0  (omitted)
             _cons |   2.866764   1.530624     1.87   0.091    -.5436789    6.277207
------------------------------------------------------------------------------------

. 
. *add scalar for pre-matching imbalance
. quietly imb list_pre gov2 frontrunner bezirksvorsitz01 parteiamt title cand_age gende
> r district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatm
> ent(affair)

. estadd scalar imbL1 = r(L1): c

. *add scalar for post-matching imbalance
. quietly cem list_pre(1.5 2.5 3.5 6.5 10.5) gov2 frontrunner bezirksvorsitz01 parteiam
> t title cand_age(20 30 45 60 80) gender district_candidate rbez_d1 rbez_d2 rbez_d3 rb
> ez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar cemL1 = r(L1): c

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': c

. estadd scalar controls = `a0': c

. *add scalar for control-group mean
. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): c

. 
. **
. *Model 2: matching on fewer controls, esp. not OLPR list length, local interest comit
> ee, incumbent since 2000 & for district candidate, frontrunner, local party leader, p
> arty functionary,  list position coarsening reduced to (1 2 3-10 else)
. 
. cem list_pre(1.5 2.5 10.5) gov2 title cand_age(20 30 45 60 80) gender rbez_d1 rbez_d2
>  rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

Matching Summary:
-----------------
Number of strata: 92
Number of matched strata: 6

             0    1
      All  150   14
  Matched   27    7
Unmatched  123    7


Multivariate L1 distance: .69727891

Univariate imbalance:

                L1      mean       min       25%       50%       75%       max
list_pre    .23129    2.4354        -2        -5         3         6        -2
    gov2   2.8e-17  -5.6e-17         0         0         0         0         0
   title   2.8e-17  -5.6e-17         0         0         0         0         0
cand_age    .28571    1.4048         6         3         1         0       -14
  gender         0         0         0         0         0         0         0
 rbez_d1         0  -1.1e-16         0         0         0         0         0
 rbez_d2         0         0         0         0         0         0         0
 rbez_d3         0   1.1e-16         0         0         0         0         0
 rbez_d4         0         0         0         0         0         0         0
 rbez_d5         0         0         0         0         0         0         0
 rbez_d6   5.6e-17  -1.1e-16         0         0         0         0         0
 rbez_d7   8.3e-17  -5.6e-17         0         0         0         0         0

. eststo e: reg sv_cand affair list_pre gov2 title cand_age gender rbez_d1 rbez_d2 rbez
> _d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 [iweight=cem_weights]
note: title omitted because of collinearity
note: gender omitted because of collinearity
note: rbez_d2 omitted because of collinearity
note: rbez_d4 omitted because of collinearity
note: rbez_d5 omitted because of collinearity
note: rbez_d7 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(7, 26)        =     21.74
       Model |  183.373306         7  26.1961865   Prob > F        =    0.0000
    Residual |  31.3266151        26  1.20486981   R-squared       =    0.8541
-------------+----------------------------------   Adj R-squared   =    0.8148
       Total |  214.699921        33  6.50605821   Root MSE        =    1.0977

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.255869   .4756138    -2.64   0.014    -2.233507   -.2782307
    list_pre |   .0085182   .0383085     0.22   0.826    -.0702261    .0872626
        gov2 |    .773575   1.188515     0.65   0.521    -1.669453    3.216603
       title |          0  (omitted)
    cand_age |  -.0176708   .0337608    -0.52   0.605    -.0870672    .0517256
      gender |          0  (omitted)
     rbez_d1 |   .9772823   .6229672     1.57   0.129    -.3032451     2.25781
     rbez_d2 |          0  (omitted)
     rbez_d3 |  -.7203377   .8477423    -0.85   0.403    -2.462897    1.022222
     rbez_d4 |          0  (omitted)
     rbez_d5 |          0  (omitted)
     rbez_d6 |   5.116928   .7463376     6.86   0.000      3.58281    6.651047
     rbez_d7 |          0  (omitted)
       _cons |   2.027197   1.970696     1.03   0.313    -2.023626     6.07802
------------------------------------------------------------------------------

. 
. *add scalar for pre-matching imbalance
. quietly imb list_pre gov2 title cand_age gender rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_
> d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar imbL1 = r(L1): e

. *add scalar for post-matching imbalance
. quietly cem list_pre(1.5 2.5 10.5) gov2 title cand_age(20 30 45 60 80) gender rbez_d1
>  rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar cemL1 = r(L1): e

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': e

. estadd scalar controls = `a0': e

. *add scalar for control-group mean
. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): e

. 
. **
. *Model 3: matching on fewer controls (comp Mod. 3, add. list position coarsening redu
> ced to (1 2 else))
. 
. cem list_pre(1.5 2.5) gov2 title gender cand_age(20 30 45 60 80) rbez_d1 rbez_d2 rbez
> _d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7  , treatment(affair)

Matching Summary:
-----------------
Number of strata: 72
Number of matched strata: 7

             0    1
      All  150   14
  Matched   36    9
Unmatched  114    5


Multivariate L1 distance: .70740741

Univariate imbalance:

                L1      mean       min       25%       50%       75%       max
list_pre    .22685    3.9898         0        -2         5         6        -2
    gov2   3.3e-16  -9.7e-17         0         0         0         0         0
   title   3.3e-16  -9.7e-17         0         0         0         0         0
  gender         0         0         0         0         0         0         0
cand_age    .43426    2.5954        11         5         6         2       -14
 rbez_d1   6.4e-16  -1.7e-16         0         0         0         0         0
 rbez_d2   6.5e-16  -1.9e-16         0         0         0         0         0
 rbez_d3   4.7e-16  -1.7e-16         0         0         0         0         0
 rbez_d4         0         0         0         0         0         0         0
 rbez_d5         0         0         0         0         0         0         0
 rbez_d6   3.2e-16  -1.9e-16         0         0         0         0         0
 rbez_d7   2.1e-16  -8.3e-17         0         0         0         0         0

. eststo g: reg sv_cand affair list_pre title cand_age gender rbez_d1 rbez_d2 rbez_d3 r
> bez_d4 rbez_d5 rbez_d6 rbez_d7 [iweight=cem_weights]
note: gender omitted because of collinearity
note: rbez_d2 omitted because of collinearity
note: rbez_d4 omitted because of collinearity
note: rbez_d5 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =        45
-------------+----------------------------------   F(8, 36)        =     12.80
       Model |  195.889697         8  24.4862121   Prob > F        =    0.0000
    Residual |  68.8576768        36  1.91271324   R-squared       =    0.7399
-------------+----------------------------------   Adj R-squared   =    0.6821
       Total |  264.747373        44  6.01698576   Root MSE        =     1.383

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.100978   .5443649    -2.02   0.051    -2.205001    .0030453
    list_pre |  -.0571382     .04061    -1.41   0.168    -.1394991    .0252227
       title |  -.8156016   1.310152    -0.62   0.538    -3.472714     1.84151
    cand_age |  -.0661769   .0396169    -1.67   0.104    -.1465237      .01417
      gender |          0  (omitted)
     rbez_d1 |   1.570657   1.060437     1.48   0.147    -.5800093    3.721323
     rbez_d2 |          0  (omitted)
     rbez_d3 |  -.0596761   1.165579    -0.05   0.959     -2.42358    2.304228
     rbez_d4 |          0  (omitted)
     rbez_d5 |          0  (omitted)
     rbez_d6 |   4.161988   .7533657     5.52   0.000     2.634091    5.689884
     rbez_d7 |   .0528545   .9518238     0.06   0.956    -1.877534    1.983243
       _cons |   5.857865   1.594573     3.67   0.001     2.623922    9.091808
------------------------------------------------------------------------------

. 
. *add scalar for pre-matching imbalance
. quietly imb list_pre gov2 title cand_age gender rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_
> d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar imbL1 = r(L1): g

. *add scalar for post-matching imbalance
. quietly cem list_pre(1.5 2.5) gov2 title gender cand_age(20 30 45 60 80) rbez_d1 rbez
> _d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7  , treatment(affair)

. estadd scalar cemL1 = r(L1): g

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': g

. estadd scalar controls = `a0': g

. *add scalar for control-group mean
. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): g

. 
. **
. *Model 4: matching only on pre-electoral list position, coarsened to 1 2 3-6 7-10 els
> e)
. 
. cem list_pre(1.5 2.5 3.5 6.5 10.5)  , treatment(affair)

Matching Summary:
-----------------
Number of strata: 6
Number of matched strata: 5

             0    1
      All  150   14
  Matched  122   14
Unmatched   28    0


Multivariate L1 distance: .11904762

Univariate imbalance:

              L1    mean     min     25%     50%     75%     max
list_pre  .11905  .60155       0       0      -2       4      -2

. 
. eststo i: reg sv_cand affair list_f list_s list_t list_pre  [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =       136
-------------+----------------------------------   F(5, 130)       =    155.11
       Model |  34518.6739         5  6903.73478   Prob > F        =    0.0000
    Residual |  5786.11596       130  44.5085843   R-squared       =    0.8564
-------------+----------------------------------   Adj R-squared   =    0.8509
       Total |  40304.7899       135  298.553999   Root MSE        =    6.6715

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -4.032459   1.883048    -2.14   0.034    -7.757844   -.3070746
      list_f |   43.99541   2.026122    21.71   0.000     39.98697    48.00385
      list_s |   11.33522   2.497481     4.54   0.000     6.394254    16.27619
      list_t |     .49601   1.948887     0.25   0.800     -3.35963     4.35165
    list_pre |   -.109894   .0716795    -1.53   0.128    -.2517034    .0319154
       _cons |   4.092782   1.417746     2.89   0.005     1.287941    6.897623
------------------------------------------------------------------------------

. 
. *add scalar for pre-matching imbalance
. quietly imb list_pre gov2  , treatment(affair)

. estadd scalar imbL1 = r(L1): i

. *add scalar for post-matching imbalance
. quietly cem list_pre(1.5 2.5 3.5 6.5 10.5) , treatment(affair)

. estadd scalar cemL1 = r(L1): i

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': i

. estadd scalar controls = `a0': i

. *add scalar for control-group mean
. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): i

. 
. **
. *Table
. 
. esttab c e g i, compress label b(2) se(2) star(* 0.05) stats(N treat controls imbL1 c
> emL1 control_mean, fmt(a3 a3) labels(N "Matched treated" "Matched controls" "Pre-Matc
> hing L1 Imbalance" "Post-Matching L1 Imbalance")) ///
> order(*affair*) keep(affair   _cons) indicate("Matching variables included as control
> s = list_?  *rbez*" ) /// cand_age *gov2 list_pre
> title({\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party List
> s - CEM Matching Solution}) ///
> note("Note: Average treatment effect on the treated for second vote shares within CSU
>  regional lists for running affair candidates with weights obtained by coarsened exac
> t matching (standard errors in parentheses). Control variables adjust for remaining i
> mbalance (L1 statistic) in the sample and improve efficiency. For Model 1, candidates
>  are matched by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else), dummies for 
> government function, being frontrunner, regional party leader, party functionary, aca
> demic title, gender, being SMD candidate, dummies for the seven regional ballots and 
> age (coarsened to 20-29, 30-44, 45-59, 60-80). For Model 2, candidates are matched by
>  ballot position (coarsened to 1, 2, 3-10, else), dummies for government function, ti
> tle, gender, regional ballot dummies and age (coarsened to 20-29, 30-44, 45-59, 60-80
> ). Model 3 differs from Model 2 by coarsening ballot position broader, only by 1, 2, 
> else. Model 4 only matches by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else)
> .") ///
> collabels("binary treat.",lhs(Dep. var.: CSU vote shares))

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - CEM 
> Matching Solution}
------------------------------------------------------------
                       (1)        (2)        (3)        (4) 
                 Ca.. se~e  Ca.. se~e  Ca.. se~e  Ca.. se~e 
Dep. var.: CSU~s      bi..       bi..       bi..       bi.. 
------------------------------------------------------------
CSU affair ~2013     -1.29*     -1.26*     -1.10      -4.03*
                    (0.41)     (0.48)     (0.54)     (1.88) 

Constant              2.87       2.03       5.86*      4.09*
                    (1.53)     (1.97)     (1.59)     (1.42) 

Matching varia~c       Yes        Yes        Yes        Yes 
------------------------------------------------------------
N                       16         34         45        136 
Matched treated          4          7          9         14 
Matched controls        12         27         36        122 
Pre-Matching L~e     0.973      0.960      0.960      0.400 
Post-Matching ~e     0.179      0.697      0.707      0.119 
control_mean         1.678      2.825      2.979      9.990 
------------------------------------------------------------
Note: Average treatment effect on the treated for second vote shares within CSU regiona
> l lists for running affair candidates with weights obtained by coarsened exact matchi
> ng (standard errors in parentheses). Control variables adjust for remaining imbalance
>  (L1 statistic) in the sample and improve efficiency. For Model 1, candidates are mat
> ched by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else), dummies for governme
> nt function, being frontrunner, regional party leader, party functionary, academic ti
> tle, gender, being SMD candidate, dummies for the seven regional ballots and age (coa
> rsened to 20-29, 30-44, 45-59, 60-80). For Model 2, candidates are matched by ballot 
> position (coarsened to 1, 2, 3-10, else), dummies for government function, title, gen
> der, regional ballot dummies and age (coarsened to 20-29, 30-44, 45-59, 60-80). Model
>  3 differs from Model 2 by coarsening ballot position broader, only by 1, 2, else. Mo
> del 4 only matches by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else).
* p<0.05

. 
. 
. 
. *********************************************
. *Table A4: 2013 Summary Statistics for Candidate Level Controls
. *********************************************
. 
. eststo clear

. estpost ttest list_f list_s list_t list_pre listN_cand gov2 frontrunner bezirksvorsit
> z01 parteiamt local_committee title cand_age gender leg incumb_2000 district_candidat
> e rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7, by(affair)

             |      e(b)   e(count)      e(se)       e(t)    e(df_t)     e(p_l) 
-------------+------------------------------------------------------------------
      list_f | -.1095238        164   .0561819   -1.94945        162   .0264843 
      list_s | -.0314286        164   .0567834  -.5534816        162   .2903484 
      list_t | -.1095238        164   .0561819   -1.94945        162   .0264843 
    list_pre |  2.017143        164   2.803326   .7195535        162   .7635817 
  listN_cand |  3.565714        164   3.034795   1.174944        162   .8791294 
        gov2 | -.3752381        164   .0728264  -5.152503        162   3.70e-07 
 frontrunner |  .0066667        164   .0218828   .3046537        162   .6194895 
bezirksvo~01 | -.1095238        164   .0561819   -1.94945        162   .0264843 
   parteiamt | -.0514286        164   .0431847  -1.190897        162   .1177179 
local_comm~e | -.3352381        164   .0881582  -3.802686        162   .0001012 
       title | -.0295238        164     .08996  -.3281883        162   .3715966 
    cand_age | -5.762857        164   3.094565  -1.862251        162   .0321894 
      gender |  .1952381        164   .1207822   1.616448        162   .9460284 
         leg |      -2.3        164   .3369382  -6.826178        162   8.30e-11 
 incumb_2000 | -.5495238        164   .0875704  -6.275223        162   1.53e-09 
district_c~e | -.4933333        164    .134441  -3.669515        162   .0001647 
     rbez_d1 | -.1390476        164   .1021146  -1.361682        162   .0875944 
     rbez_d2 | -.0495238        164   .0833395  -.5942418        162   .2765897 
     rbez_d3 |  .1371429        164   .1241136   1.104979        162   .8645964 
     rbez_d4 | -.0361905        164   .0878452    -.41198        162   .3404493 
     rbez_d5 |       .16        164   .0985825   1.623005        162   .9467337 
     rbez_d6 | -.0495238        164   .0833395  -.5942418        162   .2765897 
     rbez_d7 | -.0228571        164   .0919912  -.2484709        162   .4020424 

             |      e(p)     e(p_u)     e(N_1)    e(mu_1)     e(N_2)    e(mu_2) 
-------------+------------------------------------------------------------------
      list_f |  .0529686   .9735157        150   .0333333         14   .1428571 
      list_s |  .5806967   .7096516        150        .04         14   .0714286 
      list_t |  .0529686   .9735157        150   .0333333         14   .1428571 
    list_pre |  .4728367   .2364183        150      14.16         14   12.14286 
  listN_cand |  .2417412   .1208706        150      27.28         14   23.71429 
        gov2 |  7.39e-07   .9999996        150   .0533333         14   .4285714 
 frontrunner |  .7610209   .3805105        150   .0066667         14          0 
bezirksvo~01 |  .0529686   .9735157        150   .0333333         14   .1428571 
   parteiamt |  .2354358   .8822821        150        .02         14   .0714286 
local_comm~e |  .0002024   .9998988        150   .0933333         14   .4285714 
       title |  .7431931   .6284034        150   .1133333         14   .1428571 
    cand_age |  .0643789   .9678106        150      47.88         14   53.64286 
      gender |  .1079432   .0539716        150   .2666667         14   .0714286 
         leg |  1.66e-10          1        150         .7         14          3 
 incumb_2000 |  3.06e-09          1        150   .0933333         14   .6428571 
district_c~e |  .0003294   .9998353        150   .5066667         14          1 
     rbez_d1 |  .1751889   .9124056        150   .1466667         14   .2857143 
     rbez_d2 |  .5531794   .7234103        150   .0933333         14   .1428571 
     rbez_d3 |  .2708073   .1354036        150        .28         14   .1428571 
     rbez_d4 |  .6808987   .6595507        150   .1066667         14   .1428571 
     rbez_d5 |  .1065326   .0532663        150        .16         14          0 
     rbez_d6 |  .5531794   .7234103        150   .0933333         14   .1428571 
     rbez_d7 |  .8040848   .5979576        150        .12         14   .1428571 

. 
. esttab , label compress star(* 0.05 ) /// 
> cells("mu_1(fmt(%12.2f) label(Control)) mu_2(fmt(%12.2f) label(Treated)) b(fmt(%12.2f
> ) star label(Diff-In-2013-Means))" "mean mean se(par fmt(2))" ". . .") ///
> title({\b Table : 2013 Summary Statistics for Candidate Level Controls}) ///
> note("Note: Comparison of 2013 control variables (mean and mean difference with stand
> ard errors in parentheses) for all running affair list candidates.")

{\b Table : 2013 Summary Statistics for Candidate Level Controls}
-----------------------------------------------
                       (1)                     
                                               
                 Control~n Treated~n Diff-In~e 
-----------------------------------------------
First bal..           0.03      0.14     -0.11 
                                        (0.06) 
                                               
Second ba..           0.04      0.07     -0.03 
                                        (0.06) 
                                               
Third bal..           0.03      0.14     -0.11 
                                        (0.06) 
                                               
Absolute ..          14.16     12.14      2.02 
                                        (2.80) 
                                               
List lenght          27.28     23.71      3.57 
                                        (3.03) 
                                               
Cabinet member        0.05      0.43     -0.38*
                                        (0.07) 
                                               
CSU frontrunner       0.01      0.00      0.01 
                                        (0.02) 
                                               
Regional party~r      0.03      0.14     -0.11 
                                        (0.06) 
                                               
Party function~y      0.02      0.07     -0.05 
                                        (0.04) 
                                               
Local interest~e      0.09      0.43     -0.34*
                                        (0.09) 
                                               
Academic title        0.11      0.14     -0.03 
                                        (0.09) 
                                               
Age (in years)       47.88     53.64     -5.76 
                                        (3.09) 
                                               
Female                0.27      0.07      0.20 
                                        (0.12) 
                                               
No. of leg. pe~s      0.70      3.00     -2.30*
                                        (0.34) 
                                               
Incumbent s~2000      0.09      0.64     -0.55*
                                        (0.09) 
                                               
Candidate runs~t      0.51      1.00     -0.49*
                                        (0.13) 
                                               
rbez==   ..0000       0.15      0.29     -0.14 
                                        (0.10) 
                                               
rbez==   ..0000       0.09      0.14     -0.05 
                                        (0.08) 
                                               
rbez==   ..0000       0.28      0.14      0.14 
                                        (0.12) 
                                               
rbez==   ..0000       0.11      0.14     -0.04 
                                        (0.09) 
                                               
rbez==   ..0000       0.16      0.00      0.16 
                                        (0.10) 
                                               
rbez==   ..0000       0.09      0.14     -0.05 
                                        (0.08) 
                                               
rbez==   ..0000       0.12      0.14     -0.02 
                                        (0.09) 
                                               
-----------------------------------------------
Observations           164                     
-----------------------------------------------
Note: Comparison of 2013 control variables (mean and mean difference with standard erro
> rs in parentheses) for all running affair list candidates.

. 
. 
. 
. *********************************************
. *Table A7: Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Reg
> ression Solution - Display of All Coefficients of Table 3 and Additional Models
. *********************************************
. 
. use tables\candidates_csu_replication.dta, clear

. keep if year==2013
(73 observations deleted)

. 
. global listdum = "list_f list_s list_t" 

. 
. global list = "list_pre listN_cand"

. global role = "gov2 frontrunner bezirksvorsitz01 parteiamt local_committee"

. global demo = "title cand_age gender" 

. global comp = "i.leg incumb_2000 nachruecker incumbency_stk  district_candidate" 

. 
. eststo clear

. 
. quietly eststo a:  reg sv_cand affair $listdum , r

. quietly eststo b:  reg sv_cand affair $listdum $list $role $demo $comp , r

. quietly eststo c:  reg sv_cand affair $listdum $list $role $demo $comp i.rbez, r

. 
. quietly eststo d:  reg sv_cand affair_cont $listdum , r

. quietly eststo e:  reg sv_cand affair_cont $listdum $list $role $demo $comp , r

. quietly eststo f:  reg sv_cand affair_cont $listdum $list $role $demo $comp i.rbez, r

. 
. esttab a c d f b e, compress b(2) se(2) star(* 0.05) label order(*affair*) drop(1.rbe
> z)  /// 
> rename(affair_cont affair) /// 
> title(Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - 
> Regression Solution - Display of All Coefficients of Table 3 and Additional Models) /
> //
> note("Note: Regression with robust standard errors on 2013 second vote shares of CSU 
> candidates within their respective party list. Treatment indicator with binary (Model
> s 1, 2, 5) or continuous specification (Models 3, 4, 6). Controls, as indicated, incl
> ude dummies for first, second and third list position, absolute list position, length
>  of list, dummies for candidates being member of local interest committee, cabinet me
> mber, regional party leader, leading party functionary, CSU frontrunner, district inc
> umbent, being district candidate, having academic titles, being female, being incumbe
> nt since 2000, the number of legislative periods, age in years as well as dummies for
>  the seven OLPR ballots (electoral districts) of Bavaria.") ///
> mtitles("binary treat." "binary treat." "cont. treat." "cont. treat." "binary treat."
>  "cont. treat.") ///
> collabels("Vote share" ,lhs(Dep. var.: Within CSU list vote))

Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Regressi
> on Solution - Display of All Coefficients of Table 3 and Additional Models
----------------------------------------------------------------------------------
                       (1)        (2)        (3)        (4)        (5)        (6) 
                      bi..       bi..  co.. tr~.  co.. tr~.       bi..  co.. tr~. 
Dep. var.: Wit~e Vote sh~e  Vote sh~e  Vote sh~e  Vote sh~e  Vote sh~e  Vote sh~e 
----------------------------------------------------------------------------------
CSU affair ~2013     -3.61*     -4.69*     -4.13*     -5.47*     -4.43*     -5.06*
                    (1.77)     (2.13)     (1.94)     (2.61)     (2.19)     (2.55) 

First bal..          42.43*     31.76*     43.02*     31.32*     32.47*     32.08*
                    (5.65)     (4.56)     (5.61)     (4.58)     (4.57)     (4.58) 

Second ba..          12.64*      8.23*     13.28*      8.42*      8.58*      8.79*
                    (4.27)     (3.19)     (4.29)     (3.05)     (2.98)     (2.86) 

Third bal..           2.12*      0.15       2.79*      0.63       0.28       0.71 
                    (0.61)     (1.17)     (0.96)     (1.16)     (1.17)     (1.19) 

Absolute ..                     -0.01                 -0.00      -0.02      -0.01 
                               (0.04)                (0.04)     (0.04)     (0.04) 

List lenght                     -0.10                 -0.10      -0.11*     -0.11*
                               (0.20)                (0.19)     (0.03)     (0.03) 

Cabinet member                  -0.24                  1.04      -0.33       0.88 
                               (2.83)                (3.28)     (2.90)     (3.31) 

CSU frontrunner                 36.49*                36.46*     36.49*     36.22*
                               (7.72)                (7.81)     (7.77)     (7.79) 

Regional party~r                 5.55                  6.18*      4.90       5.32 
                               (2.98)                (2.93)     (3.08)     (3.02) 

Party function~y                 1.70                  1.02       1.21       0.68 
                               (6.53)                (6.85)     (6.51)     (6.83) 

Local interest~e                -0.12                 -0.88       0.03      -0.68 
                               (0.93)                (0.90)     (0.88)     (0.89) 

Academic title                   0.92                  1.11       1.03       1.14 
                               (1.40)                (1.35)     (1.33)     (1.29) 

Age (in years)                  -0.02                 -0.02      -0.02      -0.02 
                               (0.03)                (0.03)     (0.03)     (0.03) 

Female                           0.62                  0.69       0.64       0.63 
                               (0.48)                (0.50)     (0.49)     (0.51) 

No. of leg. pe~0                 0.00                  0.00       0.00       0.00 
                                  (.)                   (.)        (.)        (.) 

No. of leg. pe~1                 8.12                  8.81       7.54       7.96 
                               (6.01)                (6.31)     (6.12)     (6.39) 

No. of leg. pe~2                 7.45                  8.08       6.72       7.20 
                               (5.87)                (6.16)     (5.99)     (6.26) 

No. of leg. pe~3                 7.56                 10.34       6.06       8.43 
                               (6.91)                (7.38)     (6.97)     (7.40) 

No. of leg. pe~4                 9.87                 11.56       8.60       9.93 
                               (7.04)                (7.46)     (7.19)     (7.55) 

Incumbent s~2000                 2.13                  1.05       2.62       1.71 
                               (1.61)                (1.57)     (1.52)     (1.48) 

Successor from~t                -7.18                 -7.23      -6.56      -6.42 
                               (6.63)                (6.82)     (6.68)     (6.81) 

District incum~t                -6.60                 -7.54      -5.87      -6.59 
                               (5.80)                (6.15)     (5.86)     (6.19) 

Candidate runs~t                -2.19*                -2.20*     -2.17*     -2.20*
                               (0.58)                (0.58)     (0.58)     (0.58) 

OLPR ballot Ob~z                 0.82                  0.31                       
                               (1.56)                (1.51)                       

OLPR ballot Ob~n                 0.42                  0.18                       
                               (4.21)                (4.17)                       

OLPR ballot Ni~n                 0.97                  1.37                       
                               (1.38)                (1.25)                       

OLPR ballot Mi~n                -0.50                 -0.74                       
                               (0.92)                (0.79)                       

OLPR ballot Ob~n                 0.98                  0.94                       
                               (1.86)                (1.75)                       

OLPR ballot Un~n                 0.00                  0.00                       
                                  (.)                   (.)                       

Constant              2.23*      6.00       2.13*      5.72       6.45*      6.15*
                    (0.21)     (5.01)     (0.20)     (4.82)     (1.42)     (1.28) 
----------------------------------------------------------------------------------
Observations           164        164        164        164        164        164 
----------------------------------------------------------------------------------
Note: Regression with robust standard errors on 2013 second vote shares of CSU candidat
> es within their respective party list. Treatment indicator with binary (Models 1, 2, 
> 5) or continuous specification (Models 3, 4, 6). Controls, as indicated, include dumm
> ies for first, second and third list position, absolute list position, length of list
> , dummies for candidates being member of local interest committee, cabinet member, re
> gional party leader, leading party functionary, CSU frontrunner, district incumbent, 
> being district candidate, having academic titles, being female, being incumbent since
>  2000, the number of legislative periods, age in years as well as dummies for the sev
> en OLPR ballots (electoral districts) of Bavaria.
* p<0.05

. 
. 
. *********************************************
. *Table A8: Impact of Affair on Vote Shares of Candidates within CSU Party Lists - CEM
>  Matching Solution 
. *********************************************
. 
. use tables\candidates_csu_replication.dta, clear

. keep if year==2013
(73 observations deleted)

. 
. global agecut = " cand_age(20 30 45 60 80)"

. 
. eststo clear

. **
. *Model 5: very detailed matching, large imbalance reduction, no common support for ma
> ny treatment/control observations
. 
. cem list_pre(1.5 2.5 3.5 6.5 10.5) listN_cand gov2 frontrunner bezirksvorsitz01 parte
> iamt local_committee title $agecut gender leg(0 1 2 3 4) incumb_2000 district_candida
> te rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

Matching Summary:
-----------------
Number of strata: 127
Number of matched strata: 1

             0    1
      All  150   14
  Matched    2    1
Unmatched  148   13


Multivariate L1 distance: 0

Univariate imbalance:

                      L1  mean   min   25%   50%   75%   max
          list_pre     0   4.5     6     6     6     3     3
        listN_cand     0     0     0     0     0     0     0
              gov2     0     0     0     0     0     0     0
       frontrunner     0     0     0     0     0     0     0
  bezirksvorsitz01     0     0     0     0     0     0     0
         parteiamt     0     0     0     0     0     0     0
   local_committee     0     0     0     0     0     0     0
             title     0     0     0     0     0     0     0
          cand_age     0    -5    -1    -1    -1    -9    -9
            gender     0     0     0     0     0     0     0
               leg     0     0     0     0     0     0     0
       incumb_2000     0     0     0     0     0     0     0
district_candidate     0     0     0     0     0     0     0
           rbez_d1     0     0     0     0     0     0     0
           rbez_d2     0     0     0     0     0     0     0
           rbez_d3     0     0     0     0     0     0     0
           rbez_d4     0     0     0     0     0     0     0
           rbez_d5     0     0     0     0     0     0     0
           rbez_d6     0     0     0     0     0     0     0
           rbez_d7     0     0     0     0     0     0     0

. eststo a: reg sv_cand affair [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =         3
-------------+----------------------------------   F(1, 1)         =      4.35
       Model |  1.26848936         1  1.26848936   Prob > F        =    0.2846
    Residual |  .291569328         1  .291569328   R-squared       =    0.8131
-------------+----------------------------------   Adj R-squared   =    0.6262
       Total |  1.56005869         2  .780029344   Root MSE        =    .53997

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.379396   .6613274    -2.09   0.285    -9.782358    7.023566
       _cons |   2.190737   .3818176     5.74   0.110    -2.660716    7.042189
------------------------------------------------------------------------------

. 
. *scalars
. quietly imb list_pre listN_cand gov2 frontrunner bezirksvorsitz01 parteiamt local_com
> mittee title cand_age gender leg incumb_2000 district_candidate rbez_d1 rbez_d2 rbez_
> d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar imbL1 = r(L1): a

. quietly cem list_pre(1.5 2.5 3.5 6.5 10.5) listN_cand gov2 frontrunner bezirksvorsitz
> 01 parteiamt local_committee title $agecut gender leg(0 1 2 3 4) incumb_2000 district
> _candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair
> )

. estadd scalar cemL1 = r(L1): a

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': a

. estadd scalar controls = `a0': a

. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): a

. 
. **
. *Model 1,6: matching on fewer controls, esp. not OLPR list length, local interest com
> itee, incumbent since 2000 
. 
. cem list_pre(1.5 2.5 3.5 6.5 10.5) gov2 frontrunner bezirksvorsitz01 parteiamt title 
> $agecut gender district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbe
> z_d7 , treatment(affair)

Matching Summary:
-----------------
Number of strata: 114
Number of matched strata: 4

             0    1
      All  150   14
  Matched   12    4
Unmatched  138   10


Multivariate L1 distance: .17857143

Univariate imbalance:

                         L1     mean      min      25%      50%      75%      max
          list_pre   .14286   4.8036        1       -2        2        3       -2
              gov2        0        0        0        0        0        0        0
       frontrunner        0        0        0        0        0        0        0
  bezirksvorsitz01        0        0        0        0        0        0        0
         parteiamt        0        0        0        0        0        0        0
             title        0        0        0        0        0        0        0
          cand_age   .03571   1.7143        7        6        1       -1       -9
            gender        0        0        0        0        0        0        0
district_candidate        0        0        0        0        0        0        0
           rbez_d1        0  5.6e-17        0        0        0        0        0
           rbez_d2        0        0        0        0        0        0        0
           rbez_d3        0  8.3e-17        0        0        0       -1        0
           rbez_d4        0        0        0        0        0        0        0
           rbez_d5        0        0        0        0        0        0        0
           rbez_d6        0        0        0        0        0        0        0
           rbez_d7        0  2.8e-17        0        0        0        0        0

. eststo b: reg sv_cand affair [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =        16
-------------+----------------------------------   F(1, 14)        =      4.26
       Model |  4.90347615         1  4.90347615   Prob > F        =    0.0580
    Residual |  16.1004534        14  1.15003239   R-squared       =    0.2335
-------------+----------------------------------   Adj R-squared   =    0.1787
       Total |  21.0039295        15  1.40026197   Root MSE        =    1.0724

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.278473   .6191479    -2.06   0.058    -2.606413    .0494676
       _cons |   1.678064    .309574     5.42   0.000     1.014094    2.342034
------------------------------------------------------------------------------

. eststo c: reg sv_cand affair list_pre gov2 frontrunner bezirksvorsitz01 parteiamt tit
> le cand_age gender district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6
>  rbez_d7 [iweight=cem_weights]
note: gov2 omitted because of collinearity
note: frontrunner omitted because of collinearity
note: bezirksvorsitz01 omitted because of collinearity
note: parteiamt omitted because of collinearity
note: title omitted because of collinearity
note: gender omitted because of collinearity
note: district_candidate omitted because of collinearity
note: rbez_d2 omitted because of collinearity
note: rbez_d4 omitted because of collinearity
note: rbez_d5 omitted because of collinearity
note: rbez_d6 omitted because of collinearity
note: rbez_d7 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =        16
-------------+----------------------------------   F(5, 10)        =      7.74
       Model |  16.6889418         5  3.33778836   Prob > F        =    0.0032
    Residual |  4.31498775        10  .431498775   R-squared       =    0.7946
-------------+----------------------------------   Adj R-squared   =    0.6918
       Total |  21.0039295        15  1.40026197   Root MSE        =    .65689

------------------------------------------------------------------------------------
           sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
            affair |  -1.288774    .407553    -3.16   0.010    -2.196858    -.380689
          list_pre |   .0142104    .031486     0.45   0.661    -.0559448    .0843656
              gov2 |          0  (omitted)
       frontrunner |          0  (omitted)
  bezirksvorsitz01 |          0  (omitted)
         parteiamt |          0  (omitted)
             title |          0  (omitted)
          cand_age |  -.0338098    .026818    -1.26   0.236    -.0935641    .0259445
            gender |          0  (omitted)
district_candidate |          0  (omitted)
           rbez_d1 |   1.294229   .4175636     3.10   0.011     .3638394    2.224619
           rbez_d2 |          0  (omitted)
           rbez_d3 |  -.9787026   .6746656    -1.45   0.178    -2.481951     .524546
           rbez_d4 |          0  (omitted)
           rbez_d5 |          0  (omitted)
           rbez_d6 |          0  (omitted)
           rbez_d7 |          0  (omitted)
             _cons |   2.866764   1.530624     1.87   0.091    -.5436789    6.277207
------------------------------------------------------------------------------------

. 
. *scalars
. quietly imb list_pre gov2 frontrunner bezirksvorsitz01 parteiamt title cand_age gende
> r district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatm
> ent(affair)

. estadd scalar imbL1 = r(L1): b c

. quietly cem list_pre(1.5 2.5 3.5 6.5 10.5) gov2 frontrunner bezirksvorsitz01 parteiam
> t title $agecut gender district_candidate rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_d5 rbe
> z_d6 rbez_d7 , treatment(affair)

. estadd scalar cemL1 = r(L1): b c

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': b c

. estadd scalar controls = `a0': b c

. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): b c

. 
. **
. *Model 2,7: matching on fewer controls, esp. not OLPR list length, local interest com
> itee, incumbent since 2000 & for district candidate, frontrunner, local party leader,
>  party functionary,  list position coarsening reduced to (1 2 3-10 else)
. 
. cem list_pre(1.5 2.5 10.5) gov2 title $agecut gender rbez_d1 rbez_d2 rbez_d3 rbez_d4 
> rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

Matching Summary:
-----------------
Number of strata: 92
Number of matched strata: 6

             0    1
      All  150   14
  Matched   27    7
Unmatched  123    7


Multivariate L1 distance: .69727891

Univariate imbalance:

                L1      mean       min       25%       50%       75%       max
list_pre    .23129    2.4354        -2        -5         3         6        -2
    gov2   2.8e-17  -5.6e-17         0         0         0         0         0
   title   2.8e-17  -5.6e-17         0         0         0         0         0
cand_age    .28571    1.4048         6         3         1         0       -14
  gender         0         0         0         0         0         0         0
 rbez_d1         0  -1.1e-16         0         0         0         0         0
 rbez_d2         0         0         0         0         0         0         0
 rbez_d3         0   1.1e-16         0         0         0         0         0
 rbez_d4         0         0         0         0         0         0         0
 rbez_d5         0         0         0         0         0         0         0
 rbez_d6   5.6e-17  -1.1e-16         0         0         0         0         0
 rbez_d7   8.3e-17  -5.6e-17         0         0         0         0         0

. eststo d: reg sv_cand affair [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =      1.37
       Model |   8.8244446         1   8.8244446   Prob > F        =    0.2502
    Residual |  205.875476        32  6.43360863   R-squared       =    0.0411
-------------+----------------------------------   Adj R-squared   =    0.0111
       Total |  214.699921        33  6.50605821   Root MSE        =    2.5365

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.259947   1.075811    -1.17   0.250    -3.451302    .9314079
       _cons |   2.824943   .4881412     5.79   0.000     1.830632    3.819254
------------------------------------------------------------------------------

. eststo e: reg sv_cand affair list_pre gov2 title cand_age gender rbez_d1 rbez_d2 rbez
> _d3 rbez_d4 rbez_d5 rbez_d6 rbez_d7 [iweight=cem_weights]
note: title omitted because of collinearity
note: gender omitted because of collinearity
note: rbez_d2 omitted because of collinearity
note: rbez_d4 omitted because of collinearity
note: rbez_d5 omitted because of collinearity
note: rbez_d7 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(7, 26)        =     21.74
       Model |  183.373306         7  26.1961865   Prob > F        =    0.0000
    Residual |  31.3266151        26  1.20486981   R-squared       =    0.8541
-------------+----------------------------------   Adj R-squared   =    0.8148
       Total |  214.699921        33  6.50605821   Root MSE        =    1.0977

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.255869   .4756138    -2.64   0.014    -2.233507   -.2782307
    list_pre |   .0085182   .0383085     0.22   0.826    -.0702261    .0872626
        gov2 |    .773575   1.188515     0.65   0.521    -1.669453    3.216603
       title |          0  (omitted)
    cand_age |  -.0176708   .0337608    -0.52   0.605    -.0870672    .0517256
      gender |          0  (omitted)
     rbez_d1 |   .9772823   .6229672     1.57   0.129    -.3032451     2.25781
     rbez_d2 |          0  (omitted)
     rbez_d3 |  -.7203377   .8477423    -0.85   0.403    -2.462897    1.022222
     rbez_d4 |          0  (omitted)
     rbez_d5 |          0  (omitted)
     rbez_d6 |   5.116928   .7463376     6.86   0.000      3.58281    6.651047
     rbez_d7 |          0  (omitted)
       _cons |   2.027197   1.970696     1.03   0.313    -2.023626     6.07802
------------------------------------------------------------------------------

. 
. *scalars
. quietly imb list_pre gov2 title cand_age gender rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_
> d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar imbL1 = r(L1): d e

. quietly cem list_pre(1.5 2.5 10.5) gov2 title $agecut gender rbez_d1 rbez_d2 rbez_d3 
> rbez_d4 rbez_d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar cemL1 = r(L1): d e

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': d e

. estadd scalar controls = `a0': d e

. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): d e

. 
. **
. *Model 3, 8: matching on fewer controls (comp Mod. 3, add. list position coarsening r
> educed to (1 2 else))
. 
. cem list_pre(1.5 2.5) gov2 title gender $agecut rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_
> d5 rbez_d6 rbez_d7  , treatment(affair)

Matching Summary:
-----------------
Number of strata: 72
Number of matched strata: 7

             0    1
      All  150   14
  Matched   36    9
Unmatched  114    5


Multivariate L1 distance: .70740741

Univariate imbalance:

                L1      mean       min       25%       50%       75%       max
list_pre    .22685    3.9898         0        -2         5         6        -2
    gov2   3.3e-16  -9.7e-17         0         0         0         0         0
   title   3.3e-16  -9.7e-17         0         0         0         0         0
  gender         0         0         0         0         0         0         0
cand_age    .43426    2.5954        11         5         6         2       -14
 rbez_d1   6.4e-16  -1.7e-16         0         0         0         0         0
 rbez_d2   6.5e-16  -1.9e-16         0         0         0         0         0
 rbez_d3   4.7e-16  -1.7e-16         0         0         0         0         0
 rbez_d4         0         0         0         0         0         0         0
 rbez_d5         0         0         0         0         0         0         0
 rbez_d6   3.2e-16  -1.9e-16         0         0         0         0         0
 rbez_d7   2.1e-16  -8.3e-17         0         0         0         0         0

. eststo f: reg sv_cand affair [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =        45
-------------+----------------------------------   F(1, 43)        =      2.81
       Model |  16.2151696         1  16.2151696   Prob > F        =    0.1012
    Residual |  248.532204        43  5.77981869   R-squared       =    0.0612
-------------+----------------------------------   Adj R-squared   =    0.0394
       Total |  264.747373        44  6.01698576   Root MSE        =    2.4041

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.500702   .8959646    -1.67   0.101    -3.307587    .3061827
       _cons |    2.97949   .4006876     7.44   0.000     2.171427    3.787554
------------------------------------------------------------------------------

. eststo g: reg sv_cand affair list_pre title cand_age gender rbez_d1 rbez_d2 rbez_d3 r
> bez_d4 rbez_d5 rbez_d6 rbez_d7 [iweight=cem_weights]
note: gender omitted because of collinearity
note: rbez_d2 omitted because of collinearity
note: rbez_d4 omitted because of collinearity
note: rbez_d5 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =        45
-------------+----------------------------------   F(8, 36)        =     12.80
       Model |  195.889697         8  24.4862121   Prob > F        =    0.0000
    Residual |  68.8576768        36  1.91271324   R-squared       =    0.7399
-------------+----------------------------------   Adj R-squared   =    0.6821
       Total |  264.747373        44  6.01698576   Root MSE        =     1.383

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -1.100978   .5443649    -2.02   0.051    -2.205001    .0030453
    list_pre |  -.0571382     .04061    -1.41   0.168    -.1394991    .0252227
       title |  -.8156016   1.310152    -0.62   0.538    -3.472714     1.84151
    cand_age |  -.0661769   .0396169    -1.67   0.104    -.1465237      .01417
      gender |          0  (omitted)
     rbez_d1 |   1.570657   1.060437     1.48   0.147    -.5800093    3.721323
     rbez_d2 |          0  (omitted)
     rbez_d3 |  -.0596761   1.165579    -0.05   0.959     -2.42358    2.304228
     rbez_d4 |          0  (omitted)
     rbez_d5 |          0  (omitted)
     rbez_d6 |   4.161988   .7533657     5.52   0.000     2.634091    5.689884
     rbez_d7 |   .0528545   .9518238     0.06   0.956    -1.877534    1.983243
       _cons |   5.857865   1.594573     3.67   0.001     2.623922    9.091808
------------------------------------------------------------------------------

. 
. *scalars
. quietly imb list_pre gov2 title cand_age gender rbez_d1 rbez_d2 rbez_d3 rbez_d4 rbez_
> d5 rbez_d6 rbez_d7 , treatment(affair)

. estadd scalar imbL1 = r(L1): f g

. quietly cem list_pre(1.5 2.5) gov2 title gender $agecut rbez_d1 rbez_d2 rbez_d3 rbez_
> d4 rbez_d5 rbez_d6 rbez_d7  , treatment(affair)

. estadd scalar cemL1 = r(L1): f g

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': f g

. estadd scalar controls = `a0': f g

. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): f g

. 
. 
. **
. *Model 4, 9: matching only on pre-electoral list position, coarsened to 1 2 3-6 7-10 
> else)
. 
. cem list_pre(1.5 2.5 3.5 6.5 10.5)  , treatment(affair)

Matching Summary:
-----------------
Number of strata: 6
Number of matched strata: 5

             0    1
      All  150   14
  Matched  122   14
Unmatched   28    0


Multivariate L1 distance: .11904762

Univariate imbalance:

              L1    mean     min     25%     50%     75%     max
list_pre  .11905  .60155       0       0      -2       4      -2

. 
. eststo h: reg sv_cand affair [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =       136
-------------+----------------------------------   F(1, 134)       =      0.71
       Model |  210.966125         1  210.966125   Prob > F        =    0.4026
    Residual |  40093.8237       134   299.20764   R-squared       =    0.0052
-------------+----------------------------------   Adj R-squared   =   -0.0022
       Total |  40304.7899       135  298.553999   Root MSE        =    17.298

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -4.098566   4.881034    -0.84   0.403     -13.7524     5.55527
       _cons |   9.990038   1.566053     6.38   0.000     6.892658    13.08742
------------------------------------------------------------------------------

. eststo i: reg sv_cand affair list_f list_s list_t list_pre  [iweight=cem_weights]

      Source |       SS           df       MS      Number of obs   =       136
-------------+----------------------------------   F(5, 130)       =    155.11
       Model |  34518.6739         5  6903.73478   Prob > F        =    0.0000
    Residual |  5786.11596       130  44.5085843   R-squared       =    0.8564
-------------+----------------------------------   Adj R-squared   =    0.8509
       Total |  40304.7899       135  298.553999   Root MSE        =    6.6715

------------------------------------------------------------------------------
     sv_cand |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -4.032459   1.883048    -2.14   0.034    -7.757844   -.3070746
      list_f |   43.99541   2.026122    21.71   0.000     39.98697    48.00385
      list_s |   11.33522   2.497481     4.54   0.000     6.394254    16.27619
      list_t |     .49601   1.948887     0.25   0.800     -3.35963     4.35165
    list_pre |   -.109894   .0716795    -1.53   0.128    -.2517034    .0319154
       _cons |   4.092782   1.417746     2.89   0.005     1.287941    6.897623
------------------------------------------------------------------------------

. 
. *scalars
. quietly imb list_pre gov2  , treatment(affair)

. estadd scalar imbL1 = r(L1): h i

. quietly cem list_pre(1.5 2.5 3.5 6.5 10.5) , treatment(affair)

. estadd scalar cemL1 = r(L1): h i

. matrix b = r(match_table)

. local a0 = b[2,1]

. local a1 = b[2,2]

. estadd scalar treat = `a1': h i

. estadd scalar controls = `a0': h i

. quietly sum sv_cand [aweight=cem_weights] if affair==0

. estadd scalar control_mean = r(mean): h i

. 
. 
. **
. *Table
. 
. esttab c e g i a b d f h, compress label b(2) se(2) star(* 0.05) stats(N treat contro
> ls imbL1 cemL1 control_mean, fmt(a3 a3) labels(N "Matched treated" "Matched controls"
>  "Pre-Matching L1 Imbalance" "Post-Matching L1 Imbalance")) ///
> order(affair list_? list_pre cand_age *gov2 *rbez*) keep(affair list_? list_pre cand_
> age *gov2 *rbez* ) ///
> title({\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party List
> s - CEM Matching Solution - Display of All Coefficients of Table 4 and Additional Mod
> els}) ///
> note("Note: Average treatment effect on the treated for second vote shares within CSU
>  regional lists for running affair candidates with weights obtained by coarsened exac
> t matching (standard errors in parentheses). Control variables, as indicated, adjust 
> for remaining imbalance (L1 statistic) in the sample and improve efficiency. Model 1-
> 4 show Models 1-4 of Table 4 in the main text with all control variable coefficients.
>  Model 5 reports results on a matching solution drawing on all control variables as i
> ncluded in Table 3 of the main text. Models 6-9 correspond to Models 1-4, but estimat
> e the ATT without inclusion of control variables. For Model 1, candidates are matched
>  by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else), dummies for government f
> unction, being frontrunner, regional party leader, party functionary, academic title,
>  gender, being SMD candidate, dummies for the seven regional ballots and age (coarsen
> ed to 20-29, 30-44, 45-59, 60-80). For Model 2, candidates are matched by ballot posi
> tion (coarsened to 1, 2, 3-10, else), dummies for government function, title, gender,
>  regional ballot dummies and age (coarsened to 20-29, 30-44, 45-59, 60-80). Model 3 d
> iffers to Model 2 by coarsening ballot position broader, only by 1, 2, else. Model 4 
> only matches by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else).") ///
> collabels("binary treat.",lhs(Dep. var.: CSU vote shares)) 

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - CEM 
> Matching Solution - Display of All Coefficients of Table 4 and Additional Models}
---------------------------------------------------------------------------------------
> ----------------------------
                       (1)        (2)        (3)        (4)        (5)        (6)      
>   (7)        (8)        (9) 
                 Ca.. se~e  Ca.. se~e  Ca.. se~e  Ca.. se~e  Ca.. se~e  Ca.. se~e  Ca..
>  se~e  Ca.. se~e  Ca.. se~e 
Dep. var.: CSU~s      bi..       bi..       bi..       bi..       bi..       bi..      
>  bi..       bi..       bi.. 
---------------------------------------------------------------------------------------
> ----------------------------
CSU affair ~2013     -1.29*     -1.26*     -1.10      -4.03*     -1.38      -1.28      
> -1.26      -1.50      -4.10 
                    (0.41)     (0.48)     (0.54)     (1.88)     (0.66)     (0.62)     (
> 1.08)     (0.90)     (4.88) 

First bal..                                           44.00*                           
>                             
                                                     (2.03)                            
>                             

Second ba..                                           11.34*                           
>                             
                                                     (2.50)                            
>                             

Third bal..                                            0.50                            
>                             
                                                     (1.95)                            
>                             

Absolute ..           0.01       0.01      -0.06      -0.11                            
>                             
                    (0.03)     (0.04)     (0.04)     (0.07)                            
>                             

Age (in years)       -0.03      -0.02      -0.07                                       
>                             
                    (0.03)     (0.03)     (0.04)                                       
>                             

Cabinet member        0.00       0.77                                                  
>                             
                       (.)     (1.19)                                                  
>                             

rbez==   ..0000       1.29*      0.98       1.57                                       
>                             
                    (0.42)     (0.62)     (1.06)                                       
>                             

rbez==   ..0000       0.00       0.00       0.00                                       
>                             
                       (.)        (.)        (.)                                       
>                             

rbez==   ..0000      -0.98      -0.72      -0.06                                       
>                             
                    (0.67)     (0.85)     (1.17)                                       
>                             

rbez==   ..0000       0.00       0.00       0.00                                       
>                             
                       (.)        (.)        (.)                                       
>                             

rbez==   ..0000       0.00       0.00       0.00                                       
>                             
                       (.)        (.)        (.)                                       
>                             

rbez==   ..0000       0.00       5.12*      4.16*                                      
>                             
                       (.)     (0.75)     (0.75)                                       
>                             

rbez==   ..0000       0.00       0.00       0.05                                       
>                             
                       (.)        (.)     (0.95)                                       
>                             
---------------------------------------------------------------------------------------
> ----------------------------
N                       16         34         45        136          3         16      
>    34         45        136 
Matched treated          4          7          9         14          1          4      
>     7          9         14 
Matched controls        12         27         36        122          2         12      
>    27         36        122 
Pre-Matching L~e     0.973      0.960      0.960      0.400      0.993      0.973      
> 0.960      0.960      0.400 
Post-Matching ~e     0.179      0.697      0.707      0.119          0      0.179      
> 0.697      0.707      0.119 
control_mean         1.678      2.825      2.979      9.990      2.191      1.678      
> 2.825      2.979      9.990 
---------------------------------------------------------------------------------------
> ----------------------------
Note: Average treatment effect on the treated for second vote shares within CSU regiona
> l lists for running affair candidates with weights obtained by coarsened exact matchi
> ng (standard errors in parentheses). Control variables, as indicated, adjust for rema
> ining imbalance (L1 statistic) in the sample and improve efficiency. Model 1-4 show M
> odels 1-4 of Table 4 in the main text with all control variable coefficients. Model 5
>  reports results on a matching solution drawing on all control variables as included 
> in Table 3 of the main text. Models 6-9 correspond to Models 1-4, but estimate the AT
> T without inclusion of control variables. For Model 1, candidates are matched by ball
> ot position (coarsened to 1, 2, 3, 4-6, 7-10, else), dummies for government function,
>  being frontrunner, regional party leader, party functionary, academic title, gender,
>  being SMD candidate, dummies for the seven regional ballots and age (coarsened to 20
> -29, 30-44, 45-59, 60-80). For Model 2, candidates are matched by ballot position (co
> arsened to 1, 2, 3-10, else), dummies for government function, title, gender, regiona
> l ballot dummies and age (coarsened to 20-29, 30-44, 45-59, 60-80). Model 3 differs t
> o Model 2 by coarsening ballot position broader, only by 1, 2, else. Model 4 only mat
> ches by ballot position (coarsened to 1, 2, 3, 4-6, 7-10, else).
* p<0.05

. 
. *********************************************
. *Table A11: Robustness of Table 3 - Impact of Affair on Vote Shares of Candidates wit
> hin CSU Party Lists - Papke-Wooldridge Approach for Proportions 
. *********************************************
. eststo clear

. 
. eststo: glm sv_cand_prop affair $listdum  , fam(binomial) link(logit) rob
note: sv_cand_prop has noninteger values

Iteration 0:   log pseudolikelihood = -24.004695  
Iteration 1:   log pseudolikelihood = -17.400632  
Iteration 2:   log pseudolikelihood = -17.385294  
Iteration 3:   log pseudolikelihood = -17.385215  
Iteration 4:   log pseudolikelihood = -17.385215  

Generalized linear models                         No. of obs      =        164
Optimization     : ML                             Residual df     =        159
                                                  Scale parameter =          1
Deviance         =  3.976346969                   (1/df) Deviance =   .0250085
Pearson          =  4.842764442                   (1/df) Pearson  =   .0304576

Variance function: V(u) = u*(1-u/1)               [Binomial]
Link function    : g(u) = ln(u/(1-u))             [Logit]

                                                  AIC             =   .2729904
Log pseudolikelihood = -17.38521497               BIC             =  -806.9024

------------------------------------------------------------------------------
             |               Robust
sv_cand_prop |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      affair |  -.7509121   .2404573    -3.12   0.002      -1.2222   -.2796245
      list_f |   3.802532   .2522886    15.07   0.000     3.308056    4.297009
      list_s |   2.152718   .3510036     6.13   0.000     1.464764    2.840673
      list_t |   .6488219   .1579442     4.11   0.000      .339257    .9583869
       _cons |   -3.85431   .0943549   -40.85   0.000    -4.039243   -3.669378
------------------------------------------------------------------------------
(est1 stored)

. eststo: glm sv_cand_prop affair $listdum $list $role $demo $comp i.rbez , fam(binomia
> l) link(logit) rob
note: 7.rbez omitted because of collinearity
note: sv_cand_prop has noninteger values

Iteration 0:   log pseudolikelihood = -23.555252  
Iteration 1:   log pseudolikelihood = -16.395117  
Iteration 2:   log pseudolikelihood = -16.070402  
Iteration 3:   log pseudolikelihood = -16.038895  
Iteration 4:   log pseudolikelihood = -16.038752  
Iteration 5:   log pseudolikelihood = -16.038752  

Generalized linear models                         No. of obs      =        164
Optimization     : ML                             Residual df     =        137
                                                  Scale parameter =          1
Deviance         =  1.283420745                   (1/df) Deviance =    .009368
Pearson          =  1.334963754                   (1/df) Pearson  =   .0097443

Variance function: V(u) = u*(1-u/1)               [Binomial]
Link function    : g(u) = ln(u/(1-u))             [Logit]

                                                  AIC             =   .5248628
Log pseudolikelihood = -16.03875186               BIC             =  -697.3983

--------------------------------------------------------------------------------------
                     |               Robust
        sv_cand_prop |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
              affair |  -.8337521   .2755331    -3.03   0.002    -1.373787   -.2937173
              list_f |    1.80827   .3491467     5.18   0.000     1.123955    2.492585
              list_s |   1.018444   .2923173     3.48   0.000     .4455121    1.591375
              list_t |  -.1106717   .2279603    -0.49   0.627    -.5574656    .3361222
            list_pre |  -.0575345   .0121056    -4.75   0.000    -.0812611    -.033808
          listN_cand |   .0546941   .0251348     2.18   0.030     .0054307    .1039574
                gov2 |   .2364225     .34657     0.68   0.495    -.4428422    .9156872
         frontrunner |   4.224372   .5208945     8.11   0.000     3.203437    5.245306
    bezirksvorsitz01 |   .7001248   .3057133     2.29   0.022     .1009377    1.299312
           parteiamt |  -.6240243   .3721931    -1.68   0.094    -1.353509    .1054608
     local_committee |  -.0903679   .2398226    -0.38   0.706    -.5604116    .3796758
               title |   .5870721   .1880643     3.12   0.002     .2184728    .9556713
            cand_age |  -.0014103   .0054104    -0.26   0.794    -.0120146    .0091939
              gender |   .2615143   .1433572     1.82   0.068    -.0194605    .5424892
                     |
                 leg |
                  1  |   1.645649   .5008722     3.29   0.001     .6639576    2.627341
                  2  |   1.453907   .4511201     3.22   0.001     .5697275    2.338086
                  3  |   1.702293   .7543468     2.26   0.024     .2238002    3.180785
                  4  |   2.304835   .6499899     3.55   0.000     1.030878    3.578792
                     |
         incumb_2000 |  -.0061369   .3812115    -0.02   0.987    -.7532977    .7410239
         nachruecker |  -2.337552   .6535754    -3.58   0.000    -3.618537   -1.056568
      incumbency_stk |     -1.198    .435276    -2.75   0.006    -2.051126   -.3448751
  district_candidate |  -.6370847    .242301    -2.63   0.009    -1.111986   -.1621834
                     |
                rbez |
OLPR ballot Oberp..  |   .5059418   .2072175     2.44   0.015      .099803    .9120806
OLPR ballot Oberb..  |  -2.169009   .5354107    -4.05   0.000    -3.218395   -1.119623
OLPR ballot Niede..  |   .8290637   .2081464     3.98   0.000     .4211042    1.237023
OLPR ballot Mitte..  |  -.4817143   .1980035    -2.43   0.015     -.869794   -.0936345
OLPR ballot Oberf..  |   .9684581    .264497     3.66   0.000     .4500535    1.486863
OLPR ballot Unter..  |          0  (omitted)
                     |
               _cons |  -4.337643   .6039586    -7.18   0.000     -5.52138   -3.153906
--------------------------------------------------------------------------------------
(est2 stored)

. eststo: glm sv_cand_prop affair_cont $listdum  , fam(binomial) link(logit) rob
note: sv_cand_prop has noninteger values

Iteration 0:   log pseudolikelihood = -23.977968  
Iteration 1:   log pseudolikelihood = -17.391078  
Iteration 2:   log pseudolikelihood =  -17.37783  
Iteration 3:   log pseudolikelihood = -17.377764  
Iteration 4:   log pseudolikelihood = -17.377764  

Generalized linear models                         No. of obs      =        164
Optimization     : ML                             Residual df     =        159
                                                  Scale parameter =          1
Deviance         =  3.961444959                   (1/df) Deviance =   .0249147
Pearson          =  4.723080095                   (1/df) Pearson  =   .0297049

Variance function: V(u) = u*(1-u/1)               [Binomial]
Link function    : g(u) = ln(u/(1-u))             [Logit]

                                                  AIC             =   .2728996
Log pseudolikelihood = -17.37776397               BIC             =  -806.9173

------------------------------------------------------------------------------
             |               Robust
sv_cand_prop |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 affair_cont |  -.5914699   .1695938    -3.49   0.000    -.9238676   -.2590721
      list_f |   3.826756    .253442    15.10   0.000     3.330019    4.323493
      list_s |   2.198015   .3523729     6.24   0.000     1.507377    2.888653
      list_t |   .6791785   .1555732     4.37   0.000     .3742606    .9840964
       _cons |  -3.875767   .0945876   -40.98   0.000    -4.061155   -3.690379
------------------------------------------------------------------------------
(est3 stored)

. eststo: glm sv_cand_prop affair_cont $listdum $list $role $demo $comp i.rbez , fam(bi
> nomial) link(logit) rob
note: 7.rbez omitted because of collinearity
note: sv_cand_prop has noninteger values

Iteration 0:   log pseudolikelihood = -23.529517  
Iteration 1:   log pseudolikelihood = -16.377051  
Iteration 2:   log pseudolikelihood = -16.064667  
Iteration 3:   log pseudolikelihood = -16.035992  
Iteration 4:   log pseudolikelihood =  -16.03588  
Iteration 5:   log pseudolikelihood =  -16.03588  

Generalized linear models                         No. of obs      =        164
Optimization     : ML                             Residual df     =        137
                                                  Scale parameter =          1
Deviance         =  1.277677528                   (1/df) Deviance =   .0093261
Pearson          =  1.356558956                   (1/df) Pearson  =   .0099019

Variance function: V(u) = u*(1-u/1)               [Binomial]
Link function    : g(u) = ln(u/(1-u))             [Logit]

                                                  AIC             =   .5248278
Log pseudolikelihood = -16.03588025               BIC             =   -697.404

--------------------------------------------------------------------------------------
                     |               Robust
        sv_cand_prop |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
         affair_cont |  -.8137624   .2279628    -3.57   0.000    -1.260561   -.3669635
              list_f |   1.825938   .3459411     5.28   0.000     1.147906     2.50397
              list_s |   1.070738   .2954126     3.62   0.000       .49174    1.649736
              list_t |  -.0419422   .2024233    -0.21   0.836    -.4386846    .3548001
            list_pre |   -.054017   .0114824    -4.70   0.000    -.0765221   -.0315119
          listN_cand |   .0424905   .0253994     1.67   0.094    -.0072915    .0922725
                gov2 |   .3541329   .3366902     1.05   0.293    -.3057677    1.014034
         frontrunner |   4.184435   .4959141     8.44   0.000     3.212461    5.156409
    bezirksvorsitz01 |   .7198033   .3058689     2.35   0.019     .1203112    1.319295
           parteiamt |  -.6813467   .3680814    -1.85   0.064    -1.402773    .0400796
     local_committee |  -.0800412   .2225783    -0.36   0.719    -.5162866    .3562042
               title |   .6075883   .1898604     3.20   0.001     .2354688    .9797078
            cand_age |  -.0015862   .0054946    -0.29   0.773    -.0123555    .0091831
              gender |   .2586448   .1431576     1.81   0.071     -.021939    .5392286
                     |
                 leg |
                  1  |   1.628289   .5112823     3.18   0.001     .6261944    2.630384
                  2  |   1.476008   .4650615     3.17   0.002     .5645041    2.387512
                  3  |   2.313526    .906753     2.55   0.011     .5363226    4.090729
                  4  |   2.622384   .7578317     3.46   0.001     1.137061    4.107707
                     |
         incumb_2000 |  -.3266499    .468128    -0.70   0.485    -1.244164    .5908641
         nachruecker |  -2.329739   .6562692    -3.55   0.000    -3.616003   -1.043475
      incumbency_stk |  -1.263497   .4363134    -2.90   0.004    -2.118656   -.4083384
  district_candidate |  -.6539345   .2376474    -2.75   0.006    -1.119715   -.1881542
                     |
                rbez |
OLPR ballot Oberp..  |   .3427975   .2071686     1.65   0.098    -.0632454    .7488405
OLPR ballot Oberb..  |  -1.918758   .5356988    -3.58   0.000    -2.968709   -.8688078
OLPR ballot Niede..  |   .7816619   .2064078     3.79   0.000     .3771101    1.186214
OLPR ballot Mitte..  |  -.5467704   .2090393    -2.62   0.009      -.95648   -.1370609
OLPR ballot Oberf..  |   .8194325     .25361     3.23   0.001     .3223661    1.316499
OLPR ballot Unter..  |          0  (omitted)
                     |
               _cons |   -4.06447   .6026919    -6.74   0.000    -5.245724   -2.883216
--------------------------------------------------------------------------------------
(est4 stored)

. 
. esttab , compress b(2) se(2) star(* 0.05) label order(*affair*) drop(1.rbez) /// 
> rename(affair_cont affair) /// 
> title({\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party List
> s - Papke-Wooldridge Approach for Proportions/Nonlinear Modelling with Logged Depende
> nt Variable}) ///
> note("Logistic regression following Papke and Wooldridge (1996) with robust standard 
> errors on 2013 proportion of second votes of CSU candidates within their respective p
> arty list. Treatment indicator with binary or continouus specification (as indicated)
> . Controls include dummies for first, second and third list position, absolute list p
> osition, length of list, dummies for candidates being member of local interest commit
> tee, cabinet member, regional party leader, leading party functionary, CSU frontrunne
> r, district incumbent, being district candidate, academic titles, females, being incu
> mbent since 2000, the number of legislative periods, age in years as well as dummies 
> for the seven OLPR ballots (electoral districts) of Bavaria.") ///
> collabels("M1/2:binary;M3/4:cont treat.",lhs(Dep. var.: Within CSU list vote proporti
> on))

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Papk
> e-Wooldridge Approach for Proportions/Nonlinear Modelling with Logged Dependent Varia
> ble}
------------------------------------------------------------
                       (1)        (2)        (3)        (4) 
                 Pr.. se~e  Pr.. se~e  Pr.. se~e  Pr.. se~e 
Dep. var.: Wit~p      M1..       M1..       M1..       M1.. 
------------------------------------------------------------
Proportio.. se~e                                            
CSU affair ~2013     -0.75*     -0.83*     -0.59*     -0.81*
                    (0.24)     (0.28)     (0.17)     (0.23) 

First bal..           3.80*      1.81*      3.83*      1.83*
                    (0.25)     (0.35)     (0.25)     (0.35) 

Second ba..           2.15*      1.02*      2.20*      1.07*
                    (0.35)     (0.29)     (0.35)     (0.30) 

Third bal..           0.65*     -0.11       0.68*     -0.04 
                    (0.16)     (0.23)     (0.16)     (0.20) 

Absolute ..                     -0.06*                -0.05*
                               (0.01)                (0.01) 

List lenght                      0.05*                 0.04 
                               (0.03)                (0.03) 

Cabinet member                   0.24                  0.35 
                               (0.35)                (0.34) 

CSU frontrunner                  4.22*                 4.18*
                               (0.52)                (0.50) 

Regional party~r                 0.70*                 0.72*
                               (0.31)                (0.31) 

Party function~y                -0.62                 -0.68 
                               (0.37)                (0.37) 

Local interest~e                -0.09                 -0.08 
                               (0.24)                (0.22) 

Academic title                   0.59*                 0.61*
                               (0.19)                (0.19) 

Age (in years)                  -0.00                 -0.00 
                               (0.01)                (0.01) 

Female                           0.26                  0.26 
                               (0.14)                (0.14) 

No. of leg. pe~0                 0.00                  0.00 
                                  (.)                   (.) 

No. of leg. pe~1                 1.65*                 1.63*
                               (0.50)                (0.51) 

No. of leg. pe~2                 1.45*                 1.48*
                               (0.45)                (0.47) 

No. of leg. pe~3                 1.70*                 2.31*
                               (0.75)                (0.91) 

No. of leg. pe~4                 2.30*                 2.62*
                               (0.65)                (0.76) 

Incumbent s~2000                -0.01                 -0.33 
                               (0.38)                (0.47) 

Successor from~t                -2.34*                -2.33*
                               (0.65)                (0.66) 

District incum~t                -1.20*                -1.26*
                               (0.44)                (0.44) 

Candidate runs~t                -0.64*                -0.65*
                               (0.24)                (0.24) 

OLPR ballot Ob~z                 0.51*                 0.34 
                               (0.21)                (0.21) 

OLPR ballot Ob~n                -2.17*                -1.92*
                               (0.54)                (0.54) 

OLPR ballot Ni~n                 0.83*                 0.78*
                               (0.21)                (0.21) 

OLPR ballot Mi~n                -0.48*                -0.55*
                               (0.20)                (0.21) 

OLPR ballot Ob~n                 0.97*                 0.82*
                               (0.26)                (0.25) 

OLPR ballot Un~n                 0.00                  0.00 
                                  (.)                   (.) 

Constant             -3.85*     -4.34*     -3.88*     -4.06*
                    (0.09)     (0.60)     (0.09)     (0.60) 
------------------------------------------------------------
Observations           164        164        164        164 
------------------------------------------------------------
Logistic regression following Papke and Wooldridge (1996) with robust standard errors o
> n 2013 proportion of second votes of CSU candidates within their respective party lis
> t. Treatment indicator with binary or continouus specification (as indicated). Contro
> ls include dummies for first, second and third list position, absolute list position,
>  length of list, dummies for candidates being member of local interest committee, cab
> inet member, regional party leader, leading party functionary, CSU frontrunner, distr
> ict incumbent, being district candidate, academic titles, females, being incumbent si
> nce 2000, the number of legislative periods, age in years as well as dummies for the 
> seven OLPR ballots (electoral districts) of Bavaria.
* p<0.05

. 
. 
. *********************************************
. *Table A12: Robustness of Table 3 - Impact of Affair on Ranking of Candidates within 
> CSU Party Lists
. *********************************************
. 
. use tables\candidates_csu_replication.dta, clear

. keep if year==2013
(73 observations deleted)

. 
. global listdum = "list_f list_s list_t" 

. global list = "list_pre listN_cand"

. global role = "gov2 frontrunner bezirksvorsitz01 parteiamt local_committee"

. global demo = "title cand_age gender" 

. global comp = "i.leg incumb_2000 nachruecker incumbency_stk  district_candidate" 

. 
. eststo clear

. 
. eststo:  reg list_diff affair $listdum $list $role $demo $comp `r'

      Source |       SS           df       MS      Number of obs   =       164
-------------+----------------------------------   F(22, 141)      =     28.00
       Model |  15173.0008        22  689.681852   Prob > F        =    0.0000
    Residual |  3472.99925       141  24.6312003   R-squared       =    0.8137
-------------+----------------------------------   Adj R-squared   =    0.7847
       Total |       18646       163  114.392638   Root MSE        =     4.963

------------------------------------------------------------------------------------
         list_diff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
            affair |  -4.336542   1.905861    -2.28   0.024      -8.1043   -.5687848
            list_f |   .3114177   3.269274     0.10   0.924    -6.151712    6.774547
            list_s |   1.726642   2.587381     0.67   0.506    -3.388433    6.841716
            list_t |   .2919261    2.26015     0.13   0.897    -4.176237    4.760089
          list_pre |   .8964221   .0597827    14.99   0.000     .7782358    1.014608
        listN_cand |  -.5513331   .0480532   -11.47   0.000    -.6463309   -.4563353
              gov2 |   4.141548   2.363895     1.75   0.082    -.5317104    8.814805
       frontrunner |   8.602842   7.008204     1.23   0.222    -5.251898    22.45758
  bezirksvorsitz01 |   3.643386   2.723289     1.34   0.183     -1.74037    9.027142
         parteiamt |    3.44598   4.610059     0.75   0.456    -5.667792    12.55975
   local_committee |   .3864654   1.591041     0.24   0.808    -2.758914    3.531845
             title |  -.6692528   1.329644    -0.50   0.616    -3.297868    1.959362
          cand_age |   .0044387   .0424905     0.10   0.917    -.0795622    .0884395
            gender |   2.143315   .9874543     2.17   0.032     .1911858    4.095445
                   |
               leg |
                1  |   5.890606   4.642805     1.27   0.207    -3.287901    15.06911
                2  |   3.758907   4.367255     0.86   0.391    -4.874857    12.39267
                3  |   4.890447   5.955165     0.82   0.413    -6.882505     16.6634
                4  |   4.759942   5.449437     0.87   0.384    -6.013221     15.5331
                   |
       incumb_2000 |  -.5991218   2.827326    -0.21   0.832    -6.188551    4.990308
       nachruecker |   4.794202   6.204605     0.77   0.441    -7.471877    17.06028
    incumbency_stk |  -4.285198   4.387037    -0.98   0.330    -12.95807    4.387674
district_candidate |   12.46004   1.083143    11.50   0.000     10.31874    14.60134
             _cons |  -5.883671   2.172226    -2.71   0.008    -10.17801   -1.589329
------------------------------------------------------------------------------------
(est1 stored)

. eststo:  reg list_diff affair $listdum $list $role $demo $comp i.rbez `r'
note: 7.rbez omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       164
-------------+----------------------------------   F(27, 136)      =     22.34
       Model |  15215.0559        27   563.52059   Prob > F        =    0.0000
    Residual |  3430.94406       136  25.2275299   R-squared       =    0.8160
-------------+----------------------------------   Adj R-squared   =    0.7795
       Total |       18646       163  114.392638   Root MSE        =    5.0227

--------------------------------------------------------------------------------------
           list_diff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
              affair |  -4.290041   1.981154    -2.17   0.032    -8.207894   -.3721881
              list_f |   .8684056   3.375489     0.26   0.797     -5.80683    7.543641
              list_s |   2.019776   2.643793     0.76   0.446    -3.208485    7.248038
              list_t |   .3729295   2.291286     0.16   0.871    -4.158228    4.904087
            list_pre |   .8944632   .0607159    14.73   0.000     .7743938    1.014533
          listN_cand |  -.3656222   .2586954    -1.41   0.160    -.8772082    .1459637
                gov2 |   3.909157   2.415172     1.62   0.108    -.8669924    8.685306
         frontrunner |   9.751524   7.195976     1.36   0.178    -4.478956      23.982
    bezirksvorsitz01 |   3.600967   2.829806     1.27   0.205    -1.995147    9.197081
           parteiamt |   2.366796   4.846576     0.49   0.626    -7.217603     11.9512
     local_committee |   .3943823   1.622293     0.24   0.808    -2.813801    3.602565
               title |  -.5269522    1.36066    -0.39   0.699     -3.21774    2.163836
            cand_age |  -.0042888   .0441975    -0.10   0.923    -.0916921    .0831144
              gender |   2.332448   1.021462     2.28   0.024     .3124454    4.352451
                     |
                 leg |
                  1  |   6.391689   4.787893     1.33   0.184    -3.076662    15.86004
                  2  |   3.892655   4.528903     0.86   0.392    -5.063527    12.84884
                  3  |   4.694183   6.246839     0.75   0.454    -7.659321    17.04769
                  4  |   4.711032   5.698862     0.83   0.410    -6.558814    15.98088
                     |
         incumb_2000 |  -.4444837   2.895938    -0.15   0.878    -6.171378    5.282411
         nachruecker |   4.483008   6.375501     0.70   0.483    -8.124933    17.09095
      incumbency_stk |  -4.391585   4.533312    -0.97   0.334    -13.35649    4.573315
  district_candidate |   12.51158   1.097834    11.40   0.000     10.34055    14.68261
                     |
                rbez |
OLPR ballot Oberp..  |  -.2105895   2.406271    -0.09   0.930    -4.969136    4.547957
OLPR ballot Oberb..  |  -4.584772   5.432931    -0.84   0.400    -15.32872    6.159178
OLPR ballot Niede..  |   .6979257   1.996701     0.35   0.727    -3.250673    4.646524
OLPR ballot Mitte..  |  -.6629834   1.362079    -0.49   0.627    -3.356578    2.030611
OLPR ballot Oberf..  |   .4622622   2.418309     0.19   0.849    -4.320091    5.244616
OLPR ballot Unter..  |          0  (omitted)
                     |
               _cons |  -9.355292   6.273081    -1.49   0.138    -21.76069    3.050107
--------------------------------------------------------------------------------------
(est2 stored)

. eststo:  reg list_diff affair_cont $listdum $list $role $demo $comp `r'

      Source |       SS           df       MS      Number of obs   =       164
-------------+----------------------------------   F(22, 141)      =     27.57
       Model |  15128.6213        22  687.664606   Prob > F        =    0.0000
    Residual |  3517.37867       141   24.945948   R-squared       =    0.8114
-------------+----------------------------------   Adj R-squared   =    0.7819
       Total |       18646       163  114.392638   Root MSE        =    4.9946

------------------------------------------------------------------------------------
         list_diff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
       affair_cont |  -3.228917   1.768648    -1.83   0.070    -6.725412    .2675783
            list_f |   .1652973   3.296289     0.05   0.960    -6.351239    6.681834
            list_s |   1.987365    2.59929     0.76   0.446    -3.151252    7.125983
            list_t |   .3707371   2.288007     0.16   0.872    -4.152496     4.89397
          list_pre |   .8930099   .0601985    14.83   0.000     .7740015    1.012018
        listN_cand |  -.5457282   .0481765   -11.33   0.000    -.6409698   -.4504866
              gov2 |   4.350344   2.480385     1.75   0.082    -.5532067    9.253895
       frontrunner |   8.512988   7.054382     1.21   0.230    -5.433043    22.45902
  bezirksvorsitz01 |   4.060162    2.73416     1.48   0.140    -1.345085    9.465409
         parteiamt |   3.309216    4.64901     0.71   0.478    -5.881559    12.49999
   local_committee |   -.391799   1.550551    -0.25   0.801    -3.457132    2.673534
             title |  -.6034774   1.338752    -0.45   0.653    -3.250098    2.043143
          cand_age |   .0084597   .0427436     0.20   0.843    -.0760415    .0929609
            gender |   2.200274   .9928778     2.22   0.028     .2374224    4.163125
                   |
               leg |
                1  |   5.559139   4.672481     1.19   0.236    -3.678036    14.79631
                2  |   3.586581   4.402586     0.81   0.417    -5.117031    12.29019
                3  |   4.813747    6.15522     0.78   0.435    -7.354702     16.9822
                4  |   4.561573   5.538452     0.82   0.412    -6.387567    15.51071
                   |
       incumb_2000 |  -1.007814   2.880078    -0.35   0.727     -6.70153    4.685903
       nachruecker |   5.715793   6.206362     0.92   0.359    -6.553759    17.98534
    incumbency_stk |  -4.170063   4.434743    -0.94   0.349    -12.93725     4.59712
district_candidate |   12.42048   1.089808    11.40   0.000       10.266    14.57495
             _cons |   -6.18071   2.181923    -2.83   0.005    -10.49422   -1.867197
------------------------------------------------------------------------------------
(est3 stored)

. eststo:  reg list_diff affair_cont $listdum $list $role $demo $comp i.rbez `r'
note: 7.rbez omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       164
-------------+----------------------------------   F(27, 136)      =     22.13
       Model |  15188.4843        27  562.536456   Prob > F        =    0.0000
    Residual |   3457.5157       136  25.4229096   R-squared       =    0.8146
-------------+----------------------------------   Adj R-squared   =    0.7778
       Total |       18646       163  114.392638   Root MSE        =    5.0421

--------------------------------------------------------------------------------------
           list_diff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
         affair_cont |  -3.489476   1.837115    -1.90   0.060    -7.122482    .1435302
              list_f |   .6943185   3.395947     0.20   0.838    -6.021374    7.410011
              list_s |   2.247294   2.649549     0.85   0.398    -2.992351    7.486939
              list_t |   .5290225   2.314505     0.23   0.820    -4.048053    5.106097
            list_pre |   .8928952   .0610244    14.63   0.000     .7722158    1.013575
          listN_cand |  -.3610026   .2596949    -1.39   0.167    -.8745651    .1525598
                gov2 |   4.240105   2.523205     1.68   0.095    -.7496858    9.229896
         frontrunner |   9.693185   7.223626     1.34   0.182    -4.591973    23.97834
    bezirksvorsitz01 |    4.11174   2.837023     1.45   0.150    -1.498645    9.722125
           parteiamt |   2.202819   4.879617     0.45   0.652     -7.44692    11.85256
     local_committee |  -.3381519   1.583768    -0.21   0.831    -3.470149    2.793845
               title |  -.3874822   1.365999    -0.28   0.777    -3.088829    2.313865
            cand_age |  -.0015001   .0443239    -0.03   0.973    -.0891534    .0861531
              gender |   2.442103   1.022077     2.39   0.018     .4208839    4.463322
                     |
                 leg |
                  1  |   6.239634   4.814865     1.30   0.197    -3.282055    15.76132
                  2  |   3.764042   4.554267     0.83   0.410    -5.242298    12.77038
                  3  |   4.947876   6.451585     0.77   0.444    -7.810525    17.70628
                  4  |   4.738018   5.790439     0.82   0.415    -6.712927    16.18896
                     |
         incumb_2000 |  -.9585367   2.951128    -0.32   0.746    -6.794572    4.877498
         nachruecker |   5.208543   6.363199     0.82   0.414     -7.37507    17.79216
      incumbency_stk |  -4.394676   4.577444    -0.96   0.339    -13.44685    4.657497
  district_candidate |   12.48855   1.101919    11.33   0.000     10.30944    14.66766
                     |
                rbez |
OLPR ballot Oberp..  |  -.6282814   2.412498    -0.26   0.795    -5.399144    4.142581
OLPR ballot Oberb..  |  -4.629631   5.455511    -0.85   0.398    -15.41824    6.158975
OLPR ballot Niede..  |   .9876454   2.005499     0.49   0.623     -2.97835    4.953641
OLPR ballot Mitte..  |  -.6432898    1.37249    -0.47   0.640    -3.357473    2.070893
OLPR ballot Oberf..  |   .4394329   2.427744     0.18   0.857    -4.361578    5.240444
OLPR ballot Unter..  |          0  (omitted)
                     |
               _cons |  -9.602119   6.296602    -1.52   0.130    -22.05403    2.849794
--------------------------------------------------------------------------------------
(est4 stored)

. 
. esttab , compress b(2) se(2) star(* 0.05) label order(*affair*) drop(1.rbez) /// 
> rename(affair_cont affair) /// 
> title({\b Table : Impact of Affair on Ranking of Candidates within CSU Party Lists - 
> Full Specification}) ///
> note("Note: Regression with robust standard errors on 2013 difference in pre-electora
> l ballot positions and post-electoral ranking of CSU candidates within their respecti
> ve party list. Treatment indicator with binary (Models 1-2) or continuous specificati
> on (Models 3-4). Controls include dummies for first, second and third list position, 
> absolute list position, length of list, dummies for candidates being member of local 
> interest committee, cabinet member, regional party leader, leading party functionary,
>  CSU frontrunner, district incumbent, being district candidate, academic titles, fema
> les, being incumbent since 2000, the number of legislative periods, age in years as w
> ell as dummies for the seven OLPR ballots (electoral districts) of Bavaria.") ///
> mtitles("binary treat." "binary treat." "cont. treat." "cont. treat.") ///
> collabels("Diff. in ranking",lhs(Dep. var.: Within CSU list position))

{\b Table : Impact of Affair on Ranking of Candidates within CSU Party Lists - Full Spe
> cification}
------------------------------------------------------------
                       (1)        (2)        (3)        (4) 
                      bi..       bi..  co.. tr~.  co.. tr~. 
Dep. var.: Wit~n Di.. in~g  Di.. in~g  Di.. in~g  Di.. in~g 
------------------------------------------------------------
CSU affair ~2013     -4.34*     -4.29*     -3.23      -3.49 
                    (1.91)     (1.98)     (1.77)     (1.84) 

First bal..           0.31       0.87       0.17       0.69 
                    (3.27)     (3.38)     (3.30)     (3.40) 

Second ba..           1.73       2.02       1.99       2.25 
                    (2.59)     (2.64)     (2.60)     (2.65) 

Third bal..           0.29       0.37       0.37       0.53 
                    (2.26)     (2.29)     (2.29)     (2.31) 

Absolute ..           0.90*      0.89*      0.89*      0.89*
                    (0.06)     (0.06)     (0.06)     (0.06) 

List lenght          -0.55*     -0.37      -0.55*     -0.36 
                    (0.05)     (0.26)     (0.05)     (0.26) 

Cabinet member        4.14       3.91       4.35       4.24 
                    (2.36)     (2.42)     (2.48)     (2.52) 

CSU frontrunner       8.60       9.75       8.51       9.69 
                    (7.01)     (7.20)     (7.05)     (7.22) 

Regional party~r      3.64       3.60       4.06       4.11 
                    (2.72)     (2.83)     (2.73)     (2.84) 

Party function~y      3.45       2.37       3.31       2.20 
                    (4.61)     (4.85)     (4.65)     (4.88) 

Local interest~e      0.39       0.39      -0.39      -0.34 
                    (1.59)     (1.62)     (1.55)     (1.58) 

Academic title       -0.67      -0.53      -0.60      -0.39 
                    (1.33)     (1.36)     (1.34)     (1.37) 

Age (in years)        0.00      -0.00       0.01      -0.00 
                    (0.04)     (0.04)     (0.04)     (0.04) 

Female                2.14*      2.33*      2.20*      2.44*
                    (0.99)     (1.02)     (0.99)     (1.02) 

No. of leg. pe~0      0.00       0.00       0.00       0.00 
                       (.)        (.)        (.)        (.) 

No. of leg. pe~1      5.89       6.39       5.56       6.24 
                    (4.64)     (4.79)     (4.67)     (4.81) 

No. of leg. pe~2      3.76       3.89       3.59       3.76 
                    (4.37)     (4.53)     (4.40)     (4.55) 

No. of leg. pe~3      4.89       4.69       4.81       4.95 
                    (5.96)     (6.25)     (6.16)     (6.45) 

No. of leg. pe~4      4.76       4.71       4.56       4.74 
                    (5.45)     (5.70)     (5.54)     (5.79) 

Incumbent s~2000     -0.60      -0.44      -1.01      -0.96 
                    (2.83)     (2.90)     (2.88)     (2.95) 

Successor from~t      4.79       4.48       5.72       5.21 
                    (6.20)     (6.38)     (6.21)     (6.36) 

District incum~t     -4.29      -4.39      -4.17      -4.39 
                    (4.39)     (4.53)     (4.43)     (4.58) 

Candidate runs~t     12.46*     12.51*     12.42*     12.49*
                    (1.08)     (1.10)     (1.09)     (1.10) 

OLPR ballot Ob~z                -0.21                 -0.63 
                               (2.41)                (2.41) 

OLPR ballot Ob~n                -4.58                 -4.63 
                               (5.43)                (5.46) 

OLPR ballot Ni~n                 0.70                  0.99 
                               (2.00)                (2.01) 

OLPR ballot Mi~n                -0.66                 -0.64 
                               (1.36)                (1.37) 

OLPR ballot Ob~n                 0.46                  0.44 
                               (2.42)                (2.43) 

OLPR ballot Un~n                 0.00                  0.00 
                                  (.)                   (.) 

Constant             -5.88*     -9.36      -6.18*     -9.60 
                    (2.17)     (6.27)     (2.18)     (6.30) 
------------------------------------------------------------
Observations           164        164        164        164 
------------------------------------------------------------
Note: Regression with robust standard errors on 2013 difference in pre-electoral ballot
>  positions and post-electoral ranking of CSU candidates within their respective party
>  list. Treatment indicator with binary (Models 1-2) or continuous specification (Mode
> ls 3-4). Controls include dummies for first, second and third list position, absolute
>  list position, length of list, dummies for candidates being member of local interest
>  committee, cabinet member, regional party leader, leading party functionary, CSU fro
> ntrunner, district incumbent, being district candidate, academic titles, females, bei
> ng incumbent since 2000, the number of legislative periods, age in years as well as d
> ummies for the seven OLPR ballots (electoral districts) of Bavaria.
* p<0.05

. 
. 
. *********************************************
. *Table A13: Robustness of Table 3 - Impact of Affair on 2008-2013 Difference in Vote 
> Shares of Candidates within CSU Party Lists
. *********************************************
. 
. use tables\candidates_csu_replication.dta, clear

. 
. keep if rerunning == 1 
(91 observations deleted)

. 
. xtset id year, delta(5)
       panel variable:  id (strongly balanced)
        time variable:  year, 2008 to 2013
                delta:  5 units

. fvset base 2008 year 

. fvset base 0 leg

. fvset base 0 incumbency_stk

. fvset base 1 incumbency_stk

. 
. global listdum = "list_f list_s list_t " 

. global list = "list_pre" // list_pre (no cue for unobserved quality with DiD design)

. global comp = " incumbency_stk  i.leg nachruecker district_candidate" // incumb_2000 
> (omitted [constant]) incumbency_list [coll] 

. global role = "gov2  bezirksvorsitz01 parteiamt local_committee" // frontrunner (not 
> rerunning)

. global demo = " retirement young " // title gender (omitted [constant]) age(constant,
>  recoded to relevant cagtegories)

. 
. eststo clear

. 
. quietly eststo:  xtreg sv_cand affair $listdum $list $comp $demo i.year , fe cluster(
> id)

. quietly eststo:  xtreg sv_cand affair $listdum $list $comp $role $demo i.year , fe cl
> uster(id)

. quietly eststo:  xtreg sv_cand affair $listdum $list $comp $role $demo i.year##i.rbez
>   , fe cluster(id)

. quietly eststo:  xtreg sv_cand affair_cont $listdum $list $comp $demo i.year , fe clu
> ster(id)

. quietly eststo:  xtreg sv_cand affair_cont $listdum $list $comp $role $demo i.year , 
> fe cluster(id)

. quietly eststo:  xtreg sv_cand affair_cont $listdum $list $comp $role $demo i.year##i
> .rbez  , fe cluster(id)

. 
. esttab , compress b(2) se(2) star(* 0.05) label ///
> drop(2008.* 2013.year#1.rbez ?.rbez) order(*affair* $listdum $list incumbency_stk *le
> g* nachruecker $role $demo *year* ) /// 
> rename(affair_cont affair) /// 
> title(Table A13: Impact of Affair on 2008-2013 Difference in Vote Shares of Candidate
> s within CSU Party Lists) ///
> note("Note: Fixed effects regression with standard errors cluster by candidate of 200
> 8-2013 second vote shares of CSU candidates within their respective party list. Sampl
> e draws on all candidates running both in 2008 and 2013. Treatment indicator with bin
> ary (Models 1-3) or continuous specification (Models 4-6). Controls, as indicated, in
> clude dummies for first, second and third list position, dummies for candidates being
>  member of local interest committee, cabinet member, regional party leader, leading p
> arty functionary, district incumbent, dummies for the number of legislative periods (
> 1-4), having reached retirement age/being below 35 years and allowing for different t
> rends in the seven OLPR ballots (electoral districts) of Bavaria.") ///
> mtitles("binary treat." "binary treat." "binary treat." "binary treat." "cont. treat.
> " "cont. treat." "cont. treat." "cont. treat.") ///
> collabels("Vote share",lhs(Dep. var.: Within CSU list vote))

Table A13: Impact of Affair on 2008-2013 Difference in Vote Shares of Candidates within
>  CSU Party Lists
----------------------------------------------------------------------------------
                       (1)        (2)        (3)        (4)        (5)        (6) 
                      bi..       bi..       bi..       bi..  co.. tr~.  co.. tr~. 
Dep. var.: Wit~e Vote sh~e  Vote sh~e  Vote sh~e  Vote sh~e  Vote sh~e  Vote sh~e 
----------------------------------------------------------------------------------
CSU affair ~2013     -3.04      -2.79      -1.36      -2.94      -2.79      -1.43 
                    (1.63)     (1.54)     (1.11)     (1.53)     (1.61)     (1.15) 

First bal..          28.39*     28.94*     25.61*     28.88*     29.26*     25.82*
                    (3.02)     (4.09)     (3.11)     (3.24)     (4.30)     (3.25) 

Second ba..           7.56*      7.61       6.24       8.11*      7.98       6.48 
                    (3.69)     (5.23)     (4.46)     (3.89)     (5.33)     (4.43) 

Third bal..          -4.14      -4.10      -4.40      -3.91      -4.05      -4.38 
                    (3.43)     (3.33)     (2.99)     (3.35)     (3.31)     (2.99) 

Absolute ..          -0.18      -0.16      -0.13      -0.20*     -0.17      -0.13 
                    (0.09)     (0.10)     (0.10)     (0.09)     (0.10)     (0.10) 

District incum~t     -0.40      -0.51      -0.76      -0.27      -0.45      -0.78 
                    (1.32)     (1.44)     (1.34)     (1.29)     (1.39)     (1.31) 

No. of leg. pe~0      0.00       0.00       0.00       0.00       0.00       0.00 
                       (.)        (.)        (.)        (.)        (.)        (.) 

No. of leg. pe~1      1.97       2.02       2.35*      1.68       1.85       2.36*
                    (1.23)     (1.36)     (1.17)     (1.16)     (1.30)     (1.17) 

No. of leg. pe~2      3.10       3.29       3.23       2.73       3.04       3.19 
                    (2.06)     (2.08)     (1.74)     (1.91)     (1.98)     (1.75) 

No. of leg. pe~3      6.76       7.09       4.49       6.73       7.08       4.55 
                    (3.83)     (4.09)     (3.07)     (3.69)     (4.02)     (2.93) 

No. of leg. pe~4     11.53      11.90       8.05      11.57      11.95       8.09 
                    (6.08)     (6.73)     (4.86)     (5.92)     (6.73)     (4.80) 

Successor from~t     -4.78      -4.85      -1.54      -4.73      -4.67      -1.52 
                    (2.50)     (3.43)     (2.56)     (2.55)     (3.39)     (2.64) 

Cabinet member                  -1.68      -0.53                 -1.35      -0.39 
                               (2.82)     (2.34)                (2.80)     (2.30) 

Regional party~r                 1.61       1.19                  1.99       1.43 
                               (2.02)     (1.67)                (1.96)     (1.65) 

Party function~y                 2.47       4.23                  2.12       4.10 
                               (4.52)     (3.73)                (4.64)     (3.79) 

Local interest~e                -0.03      -0.98                 -0.13      -1.05 
                               (0.82)     (0.73)                (0.80)     (0.72) 

Above retireme~e     -6.91*     -7.16      -6.90*     -6.48*     -6.65      -6.67 
                    (3.24)     (4.15)     (3.45)     (3.01)     (3.93)     (3.45) 

Candidate belo~s     -1.26      -1.48       0.37      -0.97      -1.15       0.54 
                    (0.93)     (0.93)     (1.63)     (1.01)     (1.00)     (1.59) 

Year=2013            -0.81      -0.78      -1.46      -1.03      -0.98      -1.47 
                    (0.95)     (1.12)     (1.26)     (1.00)     (1.17)     (1.24) 

Year=2013 # OL~l                            1.83                             1.50 
                                          (1.45)                           (1.37) 

Year=2013 # OL~e                           -0.46                            -0.51 
                                          (1.08)                           (1.09) 

Year=2013 # OL~a                            2.04                             2.18 
                                          (1.57)                           (1.56) 

Year=2013 # OL~r                            6.18                             6.13 
                                          (3.19)                           (3.08) 

Year=2013 # OL~n                            0.37                             0.27 
                                          (1.78)                           (1.82) 

Year=2013 # OL~a                            1.15                             1.06 
                                          (1.36)                           (1.29) 

Candidate runs~t     -0.20      -0.07      -2.41       0.09       0.07      -2.42 
                    (1.76)     (2.41)     (1.89)     (1.75)     (2.38)     (1.87) 

Constant              5.10*      4.86       5.41*      4.81*      4.55       5.24*
                    (2.34)     (2.49)     (2.31)     (2.35)     (2.54)     (2.37) 
----------------------------------------------------------------------------------
Observations           146        146        146        146        146        146 
----------------------------------------------------------------------------------
Note: Fixed effects regression with standard errors cluster by candidate of 2008-2013 s
> econd vote shares of CSU candidates within their respective party list. Sample draws 
> on all candidates running both in 2008 and 2013. Treatment indicator with binary (Mod
> els 1-3) or continuous specification (Models 4-6). Controls, as indicated, include du
> mmies for first, second and third list position, dummies for candidates being member 
> of local interest committee, cabinet member, regional party leader, leading party fun
> ctionary, district incumbent, dummies for the number of legislative periods (1-4), ha
> ving reached retirement age/being below 35 years and allowing for different trends in
>  the seven OLPR ballots (electoral districts) of Bavaria.
* p<0.05

. 
. 
. *********************************************
. *Table A14: Robustness of Table 3 � Leave-one-out Analysis for Coefficient of Affair 
> Implication on Model 1 and 2 of Tables 3 and Model 2 and 4 of Tables A10 and A11 
. *********************************************
. 
. use tables\candidates_csu_replication.dta, clear

. keep if year==2013
(73 observations deleted)

. 
. global listdum = "list_f list_s list_t " 

. global list = "list_pre listN_cand"

. global role = "gov2 frontrunner bezirksvorsitz01 parteiamt local_committee"

. global demo = "title cand_age gender" 

. global comp = "i.leg incumb_2000 nachruecker incumbency_stk  district_candidate" 

. 
. eststo clear

. 
. ***********
. *Leave one out analysis
. 
. foreach s in "affair" "affair_cont" {
  2. foreach t in sv_cand list_diff {
  3. foreach x in `s' {
  4. foreach y in `t' {
  5. foreach v in 19 28 40 86 99  111 115 119 120 121 138 139 153 161 {
  6. quietly eststo without`v': quietly reg `y' `x' $listdum $list $role $demo $comp i.
> rbez if year==2013 & id!=`v', r
  7. }
  8. quietly eststo with_all: quietly reg `y' `x' $listdum $list $role $demo $comp i.rb
> ez if year==2013 , r
  9. }
 10. }
 11. 
. quietly esttab, se nostar keep(`s') // thanks to Ben Jann: http://www.stata.com/stata
> list/archive/2009-01/msg01084.html
 12. matrix C = r(coefs)
 13. eststo clear
 14. local rnames : rownames C
 15. local models : coleq C
 16. local models : list uniq models
 17. local i 0
 18. foreach name of local rnames {
 19.     local ++i
 20.     local j 0
 21.     capture matrix drop b
 22.     capture matrix drop se
 23.     foreach model of local models {
 24.         local ++j
 25.         matrix tmp = C[`i', 2*`j'-1]
 26.         if tmp[1,1]<. {
 27.             matrix colnames tmp = `model'
 28.             matrix b = nullmat(b), tmp
 29.             matrix tmp[1,1] = C[`i', 2*`j']
 30.             matrix se = nullmat(se), tmp
 31.         }
 32.     }
 33.     ereturn post b
 34.     quietly estadd matrix se
 35.     eststo `name'
 36. }
 37. 
. **
. *Models 1, 2, 4, 5 of Table A14
. esttab , compress b(2) se(2) star(* 0.05)   ///
> title({\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party List
> s - Leave-on-out analysis on Model 2 of Table X} - Depvar `t', treat `s') ///
> note("Note: Regression with robust standard errors on 2013 `t' of CSU candidates with
> in their respective party list. The treatment indicator is `s'. The reported coeffici
> ents are treatment effects, each estimated from a different regression as indicated i
> n the heading, dropping the candidate named in the respective row from the analysis."
> ) ///
> collabels("Vote share" ,lhs(Dep. var.: `t'))
 38. }
 39. }

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Leav
> e-on-out analysis on Model 2 of Table X} - Depvar sv_cand, treat affair
---------------------
                 (1) 
                     
Dep. var~d Vote sh~e 
---------------------
without19      -4.44*
              (2.22) 

without28      -4.65*
              (2.08) 

without40      -5.12*
              (2.36) 

without86      -4.76*
              (2.23) 

without99      -2.37 
              (1.85) 

without111     -4.78*
              (2.14) 

without115     -4.94*
              (2.37) 

without119     -4.77*
              (2.18) 

without120     -5.36*
              (2.16) 

without121     -5.30*
              (2.39) 

without138     -4.76*
              (2.12) 

without139     -3.99*
              (2.02) 

without153     -4.86*
              (2.18) 

without161     -4.75*
              (2.21) 

with_all       -4.69*
              (2.13) 
---------------------
N                    
---------------------
Note: Regression with robust standard errors on 2013 sv_cand of CSU candidates within t
> heir respective party list. The treatment indicator is affair. The reported coefficie
> nts are treatment effects, each estimated from a different regression as indicated in
>  the heading, dropping the candidate named in the respective row from the analysis.
* p<0.05

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Leav
> e-on-out analysis on Model 2 of Table X} - Depvar list_diff, treat affair
---------------------
                 (1) 
                     
Dep. var~f Vote sh~e 
---------------------
without19      -4.45*
              (1.56) 

without28      -4.27*
              (1.55) 

without40      -4.58*
              (1.62) 

without86      -4.21*
              (1.60) 

without99      -3.68*
              (1.57) 

without111     -4.35*
              (1.51) 

without115     -4.07*
              (1.61) 

without119     -4.45*
              (1.55) 

without120     -5.15*
              (1.53) 

without121     -4.22*
              (1.68) 

without138     -4.27*
              (1.55) 

without139     -3.86*
              (1.60) 

without153     -3.82*
              (1.57) 

without161     -4.39*
              (1.71) 

with_all       -4.29*
              (1.55) 
---------------------
N                    
---------------------
Note: Regression with robust standard errors on 2013 list_diff of CSU candidates within
>  their respective party list. The treatment indicator is affair. The reported coeffic
> ients are treatment effects, each estimated from a different regression as indicated 
> in the heading, dropping the candidate named in the respective row from the analysis.
* p<0.05

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Leav
> e-on-out analysis on Model 2 of Table X} - Depvar sv_cand, treat affair_cont
---------------------
                 (1) 
                     
Dep. var~d Vote sh~e 
---------------------
without19      -5.45 
              (2.96) 

without28      -4.78*
              (2.32) 

without40      -5.47*
              (2.61) 

without86      -5.77*
              (2.91) 

without99      -3.86 
              (2.51) 

without111     -5.54*
              (2.66) 

without115     -5.47*
              (2.61) 

without119     -5.75*
              (2.72) 

without120     -5.47*
              (2.62) 

without121     -5.47*
              (2.61) 

without138     -5.67*
              (2.64) 

without139     -4.89*
              (2.30) 

without153     -5.57*
              (2.60) 

without161     -8.19*
              (3.12) 

with_all       -5.47*
              (2.61) 
---------------------
N                    
---------------------
Note: Regression with robust standard errors on 2013 sv_cand of CSU candidates within t
> heir respective party list. The treatment indicator is affair_cont. The reported coef
> ficients are treatment effects, each estimated from a different regression as indicat
> ed in the heading, dropping the candidate named in the respective row from the analys
> is.
* p<0.05

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Leav
> e-on-out analysis on Model 2 of Table X} - Depvar list_diff, treat affair_cont
---------------------
                 (1) 
                     
Dep. var~f Vote sh~e 
---------------------
without19      -3.89*
              (1.68) 

without28      -3.34*
              (1.50) 

without40      -3.49*
              (1.44) 

without86      -3.42*
              (1.55) 

without99      -2.95*
              (1.34) 

without111     -3.53*
              (1.46) 

without115     -3.49*
              (1.44) 

without119     -3.20*
              (1.48) 

without120     -3.47*
              (1.45) 

without121     -3.49*
              (1.44) 

without138     -3.46*
              (1.42) 

without139     -3.05*
              (1.35) 

without153     -3.70*
              (1.38) 

without161     -5.05*
              (1.94) 

with_all       -3.49*
              (1.44) 
---------------------
N                    
---------------------
Note: Regression with robust standard errors on 2013 list_diff of CSU candidates within
>  their respective party list. The treatment indicator is affair_cont. The reported co
> efficients are treatment effects, each estimated from a different regression as indic
> ated in the heading, dropping the candidate named in the respective row from the anal
> ysis.
* p<0.05

. 
. foreach s in "affair" "affair_cont" {
  2. foreach z in `s' {
  3. foreach v in 19 28 40 86 99  111 115 119 120 121 138 139 153 161 {
  4. quietly eststo without`v': glm sv_cand_prop `z' $listdum $list $role $demo $comp i
> .rbez if year==2013 & id!=`v', fam(binomial) link(logit) rob
  5. }
  6. quietly eststo with_all: glm sv_cand_prop `z' $listdum $list $role $demo $comp i.r
> bez if year==2013, fam(binomial) link(logit) rob
  7. }
  8. 
. 
. quietly esttab, se nostar keep(`s') // thanks to Ben Jann: http://www.stata.com/stata
> list/archive/2009-01/msg01084.html
  9. matrix C = r(coefs)
 10. eststo clear
 11. local rnames : rownames C
 12. local models : coleq C
 13. local models : list uniq models
 14. local i 0
 15. foreach name of local rnames {
 16.     local ++i
 17.     local j 0
 18.     capture matrix drop b
 19.     capture matrix drop se
 20.     foreach model of local models {
 21.         local ++j
 22.         matrix tmp = C[`i', 2*`j'-1]
 23.         if tmp[1,1]<. {
 24.             matrix colnames tmp = `model'
 25.             matrix b = nullmat(b), tmp
 26.             matrix tmp[1,1] = C[`i', 2*`j']
 27.             matrix se = nullmat(se), tmp
 28.         }
 29.     }
 30.     ereturn post b
 31.     quietly estadd matrix se
 32.     eststo `name'
 33. }
 34. 
. *Models 3, 6 of Table A14
. esttab , compress b(2) se(2) star(* 0.05)   ///
> title({\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party List
> s - Leave-on-out analysis on Model 2 of Table X} - Depvar sv_cand_prop, treat `s') //
> /
> note("Note: Logistic regression with robust standard errors on 2013 difference in pro
> portion of second votes of CSU candidates within their respective party list.") ///
> collabels("Vote share" ,lhs(Dep. var.: sv_cand_prop))
 35. }

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Leav
> e-on-out analysis on Model 2 of Table X} - Depvar sv_cand_prop, treat affair
---------------------
                 (1) 
                     
Dep. var~p Vote sh~e 
---------------------
without19      -0.96*
              (0.33) 

without28      -0.79*
              (0.27) 

without40      -0.90*
              (0.30) 

without86      -0.82*
              (0.27) 

without99      -0.45 
              (0.31) 

without111     -0.87*
              (0.29) 

without115     -0.82*
              (0.28) 

without119     -0.83*
              (0.28) 

without120     -1.14*
              (0.30) 

without121     -0.83*
              (0.28) 

without138     -0.81*
              (0.28) 

without139     -0.71*
              (0.25) 

without153     -0.84*
              (0.28) 

without161     -0.84*
              (0.29) 

with_all       -0.83*
              (0.28) 
---------------------
N                    
---------------------
Note: Logistic regression with robust standard errors on 2013 difference in proportion 
> of second votes of CSU candidates within their respective party list.
* p<0.05

{\b Table : Impact of Affair on Vote Shares of Candidates within CSU Party Lists - Leav
> e-on-out analysis on Model 2 of Table X} - Depvar sv_cand_prop, treat affair_cont
---------------------
                 (1) 
                     
Dep. var~p Vote sh~e 
---------------------
without19      -1.13*
              (0.24) 

without28      -0.74*
              (0.24) 

without40      -0.81*
              (0.23) 

without86      -0.80*
              (0.24) 

without99      -0.52*
              (0.24) 

without111     -0.82*
              (0.23) 

without115     -0.82*
              (0.23) 

without119     -0.88*
              (0.24) 

without120     -0.81*
              (0.23) 

without121     -0.81*
              (0.23) 

without138     -0.80*
              (0.23) 

without139     -0.68*
              (0.24) 

without153     -0.81*
              (0.23) 

without161     -0.87*
              (0.24) 

with_all       -0.81*
              (0.23) 
---------------------
N                    
---------------------
Note: Logistic regression with robust standard errors on 2013 difference in proportion 
> of second votes of CSU candidates within their respective party list.
* p<0.05

. 
. 
. *********************************************
. *END
. *********************************************
. 
. log close
      name:  <unnamed>
       log:  C:\Dropbox\bavarian affair\merged data\replication\tables\replication_OLPR
> .log
  log type:  text
 closed on:  20 Nov 2015, 19:25:00
---------------------------------------------------------------------------------------
